Carlar-kaggle-dataSet-Test-appRoboflow-YoloV4.ipynb

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  "metadata": {
    "colab": {
      "name": "Bản sao của YOLOv4-Roboflow-PyTorch.ipynb",
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      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hailuu684/Convolution-net/blob/main/Carlar-kaggle-dataSet-Test-appRoboflow-YoloV4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5q3s60oQjmHa"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "In this notebook, we implement [YOLOv4](https://arxiv.org/pdf/2004.10934.pdf) for training on your own dataset in PyTorch.\n",
        "\n",
        "We also recommend reading our blog post on [Training YOLOv4 on custom data](https://blog.roboflow.ai/training-yolov4-on-a-custom-dataset/) side by side.\n",
        "\n",
        "We will take the following steps to implement YOLOv4 on our custom data:\n",
        "* Set up YOLOv4 environment\n",
        "* Download Custom Dataset via Roboflow\n",
        "* Train Custom YOLOv4 detector\n",
        "* Reload Custom YOLOv4 detector weights for inference\n",
        "\n",
        "When you are done you will have a custom detector that you can use. It will make inference like this:\n",
        "\n",
        "#### ![Chesse Image](https://i.imgur.com/cGOxffX.png)\n",
        "\n",
        "### **Reach out for support**\n",
        "\n",
        "If you run into any hurdles on your own data set or just want to share some cool results in your own domain, [reach out!](https://roboflow.ai/contact) \n",
        "\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/WHFqYSJ.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8Pr04DEjBtk"
      },
      "source": [
        "# Set up YOLOv4 Environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rX_tj9SLtake",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2349f29-a3db-48d0-df19-2e935d8ff89f"
      },
      "source": [
        "!git clone https://github.com/roboflow-ai/pytorch-YOLOv4.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pytorch-YOLOv4'...\n",
            "remote: Enumerating objects: 395, done.\u001b[K\n",
            "remote: Total 395 (delta 0), reused 0 (delta 0), pack-reused 395\u001b[K\n",
            "Receiving objects: 100% (395/395), 922.21 KiB | 22.49 MiB/s, done.\n",
            "Resolving deltas: 100% (224/224), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dcOE23gvbHj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29888c7a-0492-45c3-ec42-66b7369243a5"
      },
      "source": [
        "%cd /content/pytorch-YOLOv4\n",
        "!pip install -r requirements.txt"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/pytorch-YOLOv4\n",
            "Requirement already satisfied: numpy==1.18.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (1.18.2)\n",
            "Requirement already satisfied: torch==1.4.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (1.4.0)\n",
            "Requirement already satisfied: scikit_image==0.16.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (0.16.2)\n",
            "Requirement already satisfied: matplotlib==2.2.3 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (2.2.3)\n",
            "Requirement already satisfied: tqdm==4.43.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (4.43.0)\n",
            "Requirement already satisfied: easydict==1.9 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (1.9)\n",
            "Requirement already satisfied: Pillow==7.1.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (7.1.2)\n",
            "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 8)) (2.1)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit_image==0.16.2->-r requirements.txt (line 3)) (2.4.1)\n",
            "Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit_image==0.16.2->-r requirements.txt (line 3)) (1.4.1)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit_image==0.16.2->-r requirements.txt (line 3)) (2.5)\n",
            "Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit_image==0.16.2->-r requirements.txt (line 3)) (1.1.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3->-r requirements.txt (line 4)) (1.3.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3->-r requirements.txt (line 4)) (1.15.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3->-r requirements.txt (line 4)) (2018.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3->-r requirements.txt (line 4)) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3->-r requirements.txt (line 4)) (2.8.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3->-r requirements.txt (line 4)) (2.4.7)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorboardX->-r requirements.txt (line 8)) (3.12.4)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit_image==0.16.2->-r requirements.txt (line 3)) (4.4.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.8.0->tensorboardX->-r requirements.txt (line 8)) (54.1.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYIS9QfDyCK9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "639f32fd-9b9e-44e0-acd9-3acb54ef4100"
      },
      "source": [
        "# download yolov4 weights that have already been converted to PyTorch\n",
        "!gdown https://drive.google.com/uc?id=1fcbR0bWzYfIEdLJPzOsn4R5mlvR6IQyA"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1fcbR0bWzYfIEdLJPzOsn4R5mlvR6IQyA\n",
            "To: /content/pytorch-YOLOv4/yolov4.conv.137.pth\n",
            "170MB [00:00, 209MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvRAbH2PjLfg"
      },
      "source": [
        "# Download Custom Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YW7jPF1BOAw"
      },
      "source": [
        "## Export Your Dataset from Roboflow\n",
        "\n",
        "Roboflow enables you to export your dataset in any format you need - including for this notebook.\n",
        "\n",
        "Create a [free account](https://app.roboflow.ai). Upload your private dataset. Generate a version (applying any preprocessing and augmentations you desire). Create an export. Select **YOLOv4 PyTorch** as the export format. Click **\"Show Download code\"**, copy your link, and paste it in the next cell. Magic."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lui6tBJKvqVS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c14de91b-a2a4-4023-9531-1986c34c95c2"
      },
      "source": [
        "# REPLACE this link with your Roboflow dataset (export as YOLOv4 PyTorch format)\n",
        "!curl -L \"https://app.roboflow.com/ds/A0DTZHFhl4?key=SUIpPcsLqf\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   896  100   896    0     0   1287      0 --:--:-- --:--:-- --:--:--  1285\n",
            "100 15.6M  100 15.6M    0     0  13.7M      0  0:00:01  0:00:01 --:--:-- 13.7M\n",
            "Archive:  roboflow.zip\n",
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            "   creating: train/\n",
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            " extracting: train/batch-6-053370_jpg.rf.dea769b4d1b2a062934099644e95cf92.jpg  \n",
            " extracting: train/batch-6-053430_jpg.rf.d5b5dabd4a7fa20673eda345f817508c.jpg  \n",
            " extracting: train/batch-6-053460_jpg.rf.1e220b8adc93c7de80c14975a9d3510f.jpg  \n",
            " extracting: train/batch-6-053550_jpg.rf.9735043d85bacc7290e10cacfe314caa.jpg  \n",
            " extracting: train/batch-6-053610_jpg.rf.90ed7aace88a8ea4487cb16073308096.jpg  \n",
            " extracting: train/batch-6-053640_jpg.rf.bac6ca381f195de151b9c7e3838afb31.jpg  \n",
            " extracting: train/batch-6-053880_jpg.rf.cba0a23043b61b17125df4266a1e98ac.jpg  \n",
            " extracting: train/batch-6-053910_jpg.rf.53dad7271d093fd884240c9695ae80d9.jpg  \n",
            " extracting: train/batch-6-053940_jpg.rf.dfc8d6e5517069950cb1dc3ef0beb016.jpg  \n",
            " extracting: train/batch-6-053970_jpg.rf.a755e290bed108db4766320bfa31a702.jpg  \n",
            " extracting: train/batch-6-054030_jpg.rf.af404ed61ae34ff1189c43aea692fc1e.jpg  \n",
            " extracting: train/batch-6-054060_jpg.rf.ae342e4d2376561dbdfa77df5f7c9d0a.jpg  \n",
            " extracting: train/batch-6-054090_jpg.rf.18090b74dc54f37232342779738b225a.jpg  \n",
            " extracting: train/batch-6-054120_jpg.rf.ec018c50a9a680638fe0e4346035d4e3.jpg  \n",
            " extracting: train/batch-6-054150_jpg.rf.6fb21caf2949663ed8c9ee5f14df935c.jpg  \n",
            " extracting: train/batch-6-054180_jpg.rf.f52d3f98a777b0aef55d94ca83995cec.jpg  \n",
            " extracting: train/batch-6-054240_jpg.rf.844e8c2384e5c15cd0afcd12627aee62.jpg  \n",
            " extracting: train/batch-6-054270_jpg.rf.38645f27ef1efeb44d2ed81607825d93.jpg  \n",
            " extracting: train/batch-6-054300_jpg.rf.5b6ee1a6bf664cce88ba80dce497dade.jpg  \n",
            " extracting: train/batch-6-054330_jpg.rf.5c2405887f1572659f49481860d8bf1f.jpg  \n",
            " extracting: train/batch-6-054360_jpg.rf.f1d3e03ab16cebe602b8334e13b0300b.jpg  \n",
            " extracting: train/batch-6-054390_jpg.rf.cbaae92171d38de437c9753f449af146.jpg  \n",
            " extracting: train/batch-6-054420_jpg.rf.01861238bb1af558c9b1006fc446e5e7.jpg  \n",
            " extracting: train/batch-6-054450_jpg.rf.a11306a17545f236f0f083e276efb232.jpg  \n",
            " extracting: train/batch-6-054480_jpg.rf.ba399c4a004d887409fc621be6e88a83.jpg  \n",
            " extracting: train/batch-6-054510_jpg.rf.3dc5baa933201278cee5e5924a3e0cf5.jpg  \n",
            " extracting: train/batch-6-054540_jpg.rf.4bc91710543f0f708731c3d31c4a412f.jpg  \n",
            " extracting: train/batch-6-054600_jpg.rf.486af45f5fdac06e4291a1015ff18c96.jpg  \n",
            " extracting: train/batch-6-054630_jpg.rf.5260d6ed62445b3a1d75339a46f0f3bb.jpg  \n",
            " extracting: train/batch-6-054660_jpg.rf.c75e6e39f1c114b4f97619aab0188890.jpg  \n",
            " extracting: train/batch-6-054690_jpg.rf.85010816867b1f7c83950067c554f0dd.jpg  \n",
            " extracting: train/batch-6-059760_jpg.rf.f5e57a70667207ce2e5b9c7d1f2702f6.jpg  \n",
            " extracting: train/batch-6-060240_jpg.rf.a6056651e18753ab18fbd17d8c096e2a.jpg  \n",
            " extracting: train/batch-6-060270_jpg.rf.00d1a407d2bbaec7d18ab00a3f4b8bca.jpg  \n",
            " extracting: train/batch-6-060390_jpg.rf.6b5c681784a1194c104a0de6119d7547.jpg  \n",
            " extracting: train/batch-6-060660_jpg.rf.1995dcf2f904c6c9b2fbbeb2830ec022.jpg  \n",
            " extracting: train/batch-6-060750_jpg.rf.fc871f2b9327aaecee907a5e77078916.jpg  \n",
            " extracting: train/batch-6-060780_jpg.rf.8d6e3c1c768dfb0a571728ff02a47f2f.jpg  \n",
            " extracting: train/batch-6-061080_jpg.rf.58ed17f4095205066eefc34fc8ae6b1d.jpg  \n",
            " extracting: train/batch-6-061110_jpg.rf.051a134e1059a6192f5c183d51a6a0c1.jpg  \n",
            " extracting: train/batch-6-061140_jpg.rf.2e64f00f01c31e1fcd355e976d5583ba.jpg  \n",
            " extracting: train/batch-6-061170_jpg.rf.bba021e61eca731a7773043df8e0ce37.jpg  \n",
            " extracting: train/batch-6-061470_jpg.rf.e1c4934c5b3e281206a00fba0fb10c42.jpg  \n",
            " extracting: train/batch-6-061710_jpg.rf.1225bee0f5dee00daf19a1bbee5f74a5.jpg  \n",
            " extracting: train/batch-6-061770_jpg.rf.97b2a31000ba29c4041da91cbe7a0517.jpg  \n",
            " extracting: train/batch-6-061860_jpg.rf.5bae2d896c5d2cbeab1de5bf34465e0c.jpg  \n",
            " extracting: train/batch-6-061980_jpg.rf.1526a29e40350d245371541c2679ac9a.jpg  \n",
            " extracting: train/batch-6-062010_jpg.rf.15b03b78ac02840edd88177cd9d40253.jpg  \n",
            " extracting: train/batch-6-062580_jpg.rf.6f9d9e3c23f2b8063c73f6841c071688.jpg  \n",
            " extracting: train/batch-6-062610_jpg.rf.12073e6e3f696390f9b658341217abca.jpg  \n",
            " extracting: train/batch-6-062730_jpg.rf.b5ec3f0c4de69ee4be0857d6d7ce1b58.jpg  \n",
            " extracting: train/batch-6-062790_jpg.rf.22c70d8762023ec65f2fd7239fd79383.jpg  \n",
            " extracting: train/batch-6-062820_jpg.rf.3d5a387e348a337ea0d0ea34c5b678da.jpg  \n",
            " extracting: train/batch-6-062880_jpg.rf.4f2985c680b02a9fbfcadb6156e87cd4.jpg  \n",
            " extracting: train/batch-6-062940_jpg.rf.11e261bc62c93f520b5105ee655b07e9.jpg  \n",
            " extracting: train/batch-6-063000_jpg.rf.57a2d38494dedac6847f6f1234be287d.jpg  \n",
            " extracting: train/batch-6-063030_jpg.rf.d8fec26176e2c1403ce4ab64f5dfc91c.jpg  \n",
            " extracting: train/batch-6-063060_jpg.rf.8a5e464c8906d9c4135ab23e51a5f567.jpg  \n",
            " extracting: train/batch-6-063180_jpg.rf.e522e415e8a9bf7556950077c74535cb.jpg  \n",
            " extracting: train/batch-6-063240_jpg.rf.76ad0a829a02fb77219472aecc9cb9a7.jpg  \n",
            " extracting: train/batch-6-063270_jpg.rf.03221f6ce376182b3585be0cebddff7d.jpg  \n",
            " extracting: train/batch-6-063300_jpg.rf.99ff6632372e0926702b4a7408d19a1d.jpg  \n",
            " extracting: train/batch-6-063360_jpg.rf.a70436a8df4986ebc1c29bb7594afc77.jpg  \n",
            " extracting: train/batch-6-063390_jpg.rf.2557acc19a083753a078030e2e3834e6.jpg  \n",
            " extracting: train/batch-6-063420_jpg.rf.17cb9b998e6f0d30d4bf2255875af4be.jpg  \n",
            " extracting: train/batch-6-063570_jpg.rf.37648c61a2fe0a090c42c62a160aba2e.jpg  \n",
            " extracting: train/batch-6-063630_jpg.rf.9942b7e33b34fd90d00241c622644afc.jpg  \n",
            " extracting: train/batch-6-063660_jpg.rf.ac69d31bdce03fa7700a7a163516a72a.jpg  \n",
            " extracting: train/batch-6-063720_jpg.rf.27c769e3f17cf30bd26f7710f17b847a.jpg  \n",
            " extracting: train/batch-6-063810_jpg.rf.fc5b261d9e901e3a0c098e9c37c1c570.jpg  \n",
            " extracting: train/batch-6-063840_jpg.rf.9361fa9cf084390aeb1afee51e03fa9e.jpg  \n",
            " extracting: train/batch-6-063930_jpg.rf.56668053410b6781a8421721e864bc88.jpg  \n",
            " extracting: train/batch-6-063990_jpg.rf.a4176fd23bd1e5bb09ec4eba9ae37cc4.jpg  \n",
            " extracting: train/batch-6-064050_jpg.rf.8c8c3b33c34aa1f479f81c742ebe38d9.jpg  \n",
            " extracting: train/batch-6-064170_jpg.rf.ed9ebff71a629780896344e61abb9cf4.jpg  \n",
            " extracting: train/batch-6-064200_jpg.rf.b95d6ecf7dd6109b1888a92ceb67a955.jpg  \n",
            " extracting: train/batch-6-064230_jpg.rf.a8877bcc63ac7118f2e753c16a5c3532.jpg  \n",
            " extracting: train/batch-6-064260_jpg.rf.a059d3df8376019b0a52988fa3b50e18.jpg  \n",
            " extracting: train/batch-6-064290_jpg.rf.80a95f0b9ce1672a042f7a4ed740f57c.jpg  \n",
            " extracting: train/batch-6-064320_jpg.rf.87d34889cba81c32176c683ae6f04d28.jpg  \n",
            " extracting: train/batch-6-064440_jpg.rf.c808e33a54f3c8548740c13249537b0b.jpg  \n",
            " extracting: train/batch-6-064500_jpg.rf.0cb11244e984f644586b72b5c502608c.jpg  \n",
            " extracting: train/batch-6-064770_jpg.rf.cf3a0e0f12d0ec7cbdcde1502f16278f.jpg  \n",
            " extracting: train/batch-6-064860_jpg.rf.c19cfc77b94c5a2f10f51e6b45e87730.jpg  \n",
            " extracting: train/batch-6-064980_jpg.rf.ca85f76aea58d5ea28d73ef399a0b522.jpg  \n",
            " extracting: train/batch-6-065010_jpg.rf.cc104cbe0d10bff9f6f9fa3d5256506c.jpg  \n",
            " extracting: train/batch-6-065070_jpg.rf.ea43b59ba9537a33febbd074ac578f28.jpg  \n",
            " extracting: train/batch-6-065400_jpg.rf.22eec8b8cd997974639d875595655e45.jpg  \n",
            " extracting: train/batch-6-065430_jpg.rf.fe73354efcbe25ba864c905be28d5d47.jpg  \n",
            " extracting: train/batch-6-065460_jpg.rf.48a5e7ade6f0ca32241522eb431a1875.jpg  \n",
            " extracting: train/batch-6-065550_jpg.rf.1bcd4e9b9788b1de1415c43031a4558f.jpg  \n",
            " extracting: train/batch-6-065610_jpg.rf.9afe561b3c10d3aae33e9ca6fd3da13c.jpg  \n",
            " extracting: train/batch-6-065640_jpg.rf.0311a39d5dd75ccbf6ea02e8006d32ee.jpg  \n",
            " extracting: train/batch-6-065670_jpg.rf.d5e01efe20e75273263f7db4ea2c85b8.jpg  \n",
            " extracting: train/batch-6-065700_jpg.rf.755c16f6ca363977868cd4a88cab9831.jpg  \n",
            " extracting: train/batch-6-065730_jpg.rf.999033aaa8c3313d97856e4fc64b20fc.jpg  \n",
            " extracting: train/batch-6-065760_jpg.rf.c4b86ea370c0a4660bfa2ffd04914d4f.jpg  \n",
            " extracting: train/batch-6-065850_jpg.rf.aca97d302acaf4df9d50282191293103.jpg  \n",
            " extracting: train/batch-6-065880_jpg.rf.26e2d7e4cc6430f983d7eb3a4cd85f2f.jpg  \n",
            " extracting: train/batch-6-065910_jpg.rf.06cc519cd98e7d3679fac9cea42307bb.jpg  \n",
            " extracting: train/batch-6-066060_jpg.rf.8cc720445f87fe731c1d21e7d0230914.jpg  \n",
            " extracting: train/batch-6-066090_jpg.rf.a17237007b4fd664ffde659966d166db.jpg  \n",
            " extracting: train/batch-6-066120_jpg.rf.ef886137818b6f746603520f1d935811.jpg  \n",
            " extracting: train/batch-6-066150_jpg.rf.3c83f9feafb940abfea65922b44684a0.jpg  \n",
            " extracting: train/batch-6-066180_jpg.rf.e3f3ffd3e062ca5f2be7bfff023954ee.jpg  \n",
            " extracting: train/batch-6-066210_jpg.rf.71e3614a061dee96fe78562619c9995a.jpg  \n",
            " extracting: train/batch-6-066240_jpg.rf.671a2093e5502ba5990bae73e4f65f5f.jpg  \n",
            " extracting: train/batch-6-066270_jpg.rf.f642465f9e5a3e1ab9dd3163b7d8c95f.jpg  \n",
            " extracting: train/batch-6-066390_jpg.rf.cc4f849e5b269212706c3e8c67c63d76.jpg  \n",
            " extracting: train/batch-6-066420_jpg.rf.d65a92cfd3ae3f403690b4214024a073.jpg  \n",
            " extracting: train/batch-6-066480_jpg.rf.342469a2b7f2b7083bb1a4c69df98b21.jpg  \n",
            " extracting: train/batch-6-066510_jpg.rf.8adabf660700df0840d8d1617824cc2a.jpg  \n",
            " extracting: train/batch-6-066540_jpg.rf.133d19f4be12dd85ac23dab1320d98bf.jpg  \n",
            " extracting: train/batch-6-066570_jpg.rf.ada4da24f142e7e717a12cc6a242d1c4.jpg  \n",
            " extracting: train/batch-6-066600_jpg.rf.f8bdeb120399a47c1425d8482134f613.jpg  \n",
            " extracting: train/batch-6-066690_jpg.rf.fa7f039e04339ef61a27331888d470c0.jpg  \n",
            " extracting: train/batch-6-066720_jpg.rf.2112e52fc77b4cc1ad2c6ed9c4981361.jpg  \n",
            " extracting: train/batch-6-066780_jpg.rf.38e060a313cb43ee9712c2df376836c9.jpg  \n",
            " extracting: train/batch-6-066810_jpg.rf.27788bd0bae6a4623d7ef650a022feef.jpg  \n",
            " extracting: train/batch-6-066840_jpg.rf.d3a71fc7523a1cdbb73eb4c2e1c5b8f0.jpg  \n",
            " extracting: train/batch-6-066960_jpg.rf.5783b8b4cb2a50120ef5208598c7ed1b.jpg  \n",
            " extracting: train/batch-6-066990_jpg.rf.96ecff04c4d7180dfa9c8af6c6cae440.jpg  \n",
            " extracting: train/batch-6-067230_jpg.rf.6dcfe0116a68b6bfa6dc7e8a49c9d5d0.jpg  \n",
            " extracting: train/batch-6-067440_jpg.rf.66b2c24de338cc805a975eb6e5d8a140.jpg  \n",
            " extracting: train/batch-6-067770_jpg.rf.0fd3a74ca63537bafadc19febb2aa95d.jpg  \n",
            " extracting: train/batch-6-067830_jpg.rf.299b21a9cde17536220e17022410bc68.jpg  \n",
            " extracting: train/batch-6-067860_jpg.rf.4992f0080ec91f49f3425e49915f23ba.jpg  \n",
            " extracting: train/batch-6-068160_jpg.rf.2ddc53b7da9d65e9d2f8e9d3a1f11ed5.jpg  \n",
            " extracting: train/batch-6-068190_jpg.rf.c7228cb91771ac1f2a32914253f95569.jpg  \n",
            " extracting: train/batch-6-068220_jpg.rf.52c2103ea87ee922110e468a7249dab1.jpg  \n",
            " extracting: train/batch-6-068340_jpg.rf.894547e6a2a58a7aaf4622a6bb98db08.jpg  \n",
            " extracting: train/batch-6-068370_jpg.rf.ae540b1f116de95d04d400f4ed165a1c.jpg  \n",
            " extracting: train/batch-6-068610_jpg.rf.13551c258392f3ba9ab930c3e8260313.jpg  \n",
            " extracting: train/batch-6-068700_jpg.rf.08ce9e6bbb65f5c7e4a9e5def6e68365.jpg  \n",
            " extracting: train/batch-6-068760_jpg.rf.53d3464f1fbdf41656b342e0fc1a7d19.jpg  \n",
            " extracting: train/batch-6-068790_jpg.rf.3528fc12c5a9e741732f44e4185cab88.jpg  \n",
            " extracting: train/batch-6-068880_jpg.rf.6f375cf35a9d9c68b3e146d4d648f34c.jpg  \n",
            " extracting: train/batch-6-068970_jpg.rf.8c778f895520b8b92930cc77fb207bf5.jpg  \n",
            " extracting: train/batch-6-069000_jpg.rf.ab225a55f8c643a4d36bc6ea4531ec20.jpg  \n",
            " extracting: train/batch-6-069030_jpg.rf.1c628efb232f4c8558afda5b817868a8.jpg  \n",
            " extracting: train/batch-6-069060_jpg.rf.1b1d199da8a98c972221eae515fdb93e.jpg  \n",
            " extracting: train/batch-6-069300_jpg.rf.18cf40ea0171a660a713959fab792d3c.jpg  \n",
            " extracting: train/batch-6-069330_jpg.rf.511589a9f6cbd2a2ff86a5483f245729.jpg  \n",
            " extracting: train/batch-6-069390_jpg.rf.ad2635c9b63f0a2bc74cbe6d6b8ab40c.jpg  \n",
            " extracting: train/batch-6-069570_jpg.rf.6914fadc1fb0659bfd9bca56c0f2f0e8.jpg  \n",
            " extracting: train/batch-6-069660_jpg.rf.daf3de1bcc85c5357f265180f2da3409.jpg  \n",
            " extracting: train/batch-6-069690_jpg.rf.24fe541ae6929d9c6c7536e6158528ca.jpg  \n",
            " extracting: train/batch-6-069720_jpg.rf.0d67bd29d3bd6287aaed6fd7d95614f2.jpg  \n",
            " extracting: train/batch-6-070020_jpg.rf.cfdae0ac5a7e486fafa2dc66ab5a6c3d.jpg  \n",
            " extracting: train/batch-6-070050_jpg.rf.6987a510d4a6137fe6e6271fe08570ba.jpg  \n",
            " extracting: train/batch-6-070080_jpg.rf.cbc78415b2b6dac0e0274905d4682171.jpg  \n",
            " extracting: train/batch-6-070230_jpg.rf.f7c5917eb39c8bc7e73cfba4c55c52c7.jpg  \n",
            " extracting: train/batch-6-070440_jpg.rf.705a91edd52561a572308aadd2af6673.jpg  \n",
            " extracting: train/batch-6-070470_jpg.rf.d485e9792cad156425d95a9aad6ec492.jpg  \n",
            " extracting: train/batch-6-070500_jpg.rf.0a347cbfbac2adc921fc86e390a6f268.jpg  \n",
            " extracting: train/batch-6-070530_jpg.rf.9af7d1a82831ee01ecdd6ce67f78aeaf.jpg  \n",
            " extracting: train/batch-6-070590_jpg.rf.50c4d442efab6701948d1302ad7547ef.jpg  \n",
            " extracting: train/batch-6-070650_jpg.rf.3c3f66ba044ef36ac388b0c3518cccb5.jpg  \n",
            " extracting: train/batch-6-070680_jpg.rf.a3a699d2ed2bfb7683bbbdfd64591c73.jpg  \n",
            " extracting: train/batch-6-070710_jpg.rf.d1f9b659ef2f2aa9e5dc48ab3557b66b.jpg  \n",
            " extracting: train/batch-6-070740_jpg.rf.44fbc5d5dc4250e96014c83b0ddbf262.jpg  \n",
            " extracting: train/batch-6-070800_jpg.rf.3e50d42b942c09358b9dc255962844d6.jpg  \n",
            " extracting: train/batch-6-070920_jpg.rf.f69a001a18fbf579619ec74b3ef3b2d3.jpg  \n",
            " extracting: train/batch-6-070980_jpg.rf.b2c8bc43ca44c7b02aa11cb593eb62ac.jpg  \n",
            " extracting: train/batch-6-071010_jpg.rf.df7966f165ae7822fecb7fdf3fdbb599.jpg  \n",
            " extracting: train/batch-6-071490_jpg.rf.4432f6dd2d5cd8c043fb693307b38585.jpg  \n",
            " extracting: train/batch-6-071580_jpg.rf.c53dd313b750d2d560693b4acf058ddd.jpg  \n",
            " extracting: train/batch-6-071610_jpg.rf.0aedaf285484852b1ba3dd9a26e5b348.jpg  \n",
            " extracting: train/batch-6-072780_jpg.rf.c9ad8e35a825197888720b41fe22a5e2.jpg  \n",
            " extracting: train/batch-6-072810_jpg.rf.6b0d73e47b2cdfa04e5b99d0f05a53dd.jpg  \n",
            " extracting: train/batch-6-072870_jpg.rf.bd703dd4d0f24a77f70ea22226d9a88e.jpg  \n",
            " extracting: train/batch-6-073080_jpg.rf.0a1f6d0538b856cd6fd70caa912a6547.jpg  \n",
            " extracting: train/batch-6-073140_jpg.rf.9dde293242ec4cf5a689d76dd223a2c7.jpg  \n",
            " extracting: train/batch-6-073200_jpg.rf.f9434c2bd37a87662204d1b3e3e1e329.jpg  \n",
            " extracting: train/batch-6-073680_jpg.rf.639991306cc8456b56848640a8445878.jpg  \n",
            " extracting: train/batch-6-073710_jpg.rf.7c5fc64a2dface26b6faf33f61df6bb6.jpg  \n",
            " extracting: train/batch-6-073740_jpg.rf.c3d7cf245ca5f98a5ab64384f8a0d0a0.jpg  \n",
            " extracting: train/batch-6-073800_jpg.rf.c47ae90eddad733cc17c415428b1afbe.jpg  \n",
            " extracting: train/batch-6-073830_jpg.rf.006cf784e7da5f186b25594eeda395cf.jpg  \n",
            " extracting: train/batch-6-073980_jpg.rf.1d3d1e25006e27a82deddbcd17f036aa.jpg  \n",
            " extracting: train/batch-6-074040_jpg.rf.ea46e341ab2bfb30e3aa31e0b3c70aa2.jpg  \n",
            " extracting: train/batch-6-074160_jpg.rf.26b7a41db493e8e8d53a4b7f45e80916.jpg  \n",
            " extracting: train/batch-6-074280_jpg.rf.490ca00ca32cd40068b0867c7d5a6e23.jpg  \n",
            " extracting: train/batch-6-074550_jpg.rf.dff60959967fe4da06d9467895f11532.jpg  \n",
            " extracting: train/batch-6-074580_jpg.rf.d715847b3c907a15ea6e86a754030c91.jpg  \n",
            " extracting: train/batch-6-074610_jpg.rf.6efdb28f41f9155645a44ccc28ddd453.jpg  \n",
            " extracting: train/batch-6-074640_jpg.rf.4d25b0348b9a3b994740d78987371886.jpg  \n",
            " extracting: train/batch-6-074880_jpg.rf.81a91cb3e5e744d19898a5d2db728b58.jpg  \n",
            " extracting: train/batch-6-074910_jpg.rf.215b7f049e461b347e1702189c2950ef.jpg  \n",
            " extracting: train/batch-6-074970_jpg.rf.51fe15cce982a8fb05f98080cfc3bb63.jpg  \n",
            " extracting: train/batch-6-075090_jpg.rf.cec330f38cd97a70ec8bdb4d1926ca85.jpg  \n",
            " extracting: train/batch-6-075180_jpg.rf.1ca731ed977516a1201010b35c5528b0.jpg  \n",
            " extracting: train/batch-6-075210_jpg.rf.9a4dfdb58ba790f10ccbbd6f86c592b6.jpg  \n",
            " extracting: train/batch-6-075240_jpg.rf.25783ac1bd21217313b362737e1ee31e.jpg  \n",
            " extracting: train/batch-6-075270_jpg.rf.4eebc627df496dadb6c483ae0e2e125e.jpg  \n",
            " extracting: train/batch-6-075360_jpg.rf.96c88ce5569a6d01ddef28cb1c1ed98b.jpg  \n",
            " extracting: train/batch-6-075480_jpg.rf.17b3c9f47e2ffa6049646d7858d97130.jpg  \n",
            " extracting: train/batch-6-075570_jpg.rf.c83e5d23ae08b5ce7b18cc7457b21e2e.jpg  \n",
            " extracting: train/batch-6-075660_jpg.rf.40d921eef196073aafe76db08cdbe3fd.jpg  \n",
            " extracting: train/batch-6-075690_jpg.rf.ea22c0632d7cc6e369dbf16cedc065a7.jpg  \n",
            " extracting: train/batch-6-075720_jpg.rf.0fe25eff9bffc1a4e75581a1caa27ed3.jpg  \n",
            " extracting: train/batch-6-075810_jpg.rf.89f83d9e6e758dad016a0c6c877bbc3c.jpg  \n",
            " extracting: train/batch-6-075870_jpg.rf.7c8fd9e7d65295d605451414c91c2579.jpg  \n",
            " extracting: train/batch-6-075930_jpg.rf.e0979c7a39f91106b9d7bcfb9a7bc2a0.jpg  \n",
            " extracting: train/batch-6-075960_jpg.rf.73e2c2cc0d330b8d65b6255ed94139bd.jpg  \n",
            " extracting: train/batch-6-076050_jpg.rf.d7a44945e3ddfd8795da067ea6c45134.jpg  \n",
            " extracting: train/batch-6-076170_jpg.rf.f45d910cfcc8f149e9ddfcade329b723.jpg  \n",
            " extracting: train/batch-6-076200_jpg.rf.23936641cf4362637c8ad4b35b2f9999.jpg  \n",
            " extracting: train/batch-6-076740_jpg.rf.32f194ecc94edf8e728664ab7df7bc66.jpg  \n",
            " extracting: train/batch-6-076770_jpg.rf.cb385ee7c17dc62f59954431af1ba7b9.jpg  \n",
            " extracting: train/batch-6-076800_jpg.rf.17407143268d98aaa5413c0ac5b2cb93.jpg  \n",
            " extracting: train/batch-6-076830_jpg.rf.8e614cc6941beb6567947fa26181c478.jpg  \n",
            " extracting: train/batch-6-076890_jpg.rf.8f916cbd5512ceff05b9c75dcda50a12.jpg  \n",
            " extracting: train/batch-6-076980_jpg.rf.be08db9c7d26efe0356b203bf1abbb28.jpg  \n",
            " extracting: train/batch-6-077010_jpg.rf.22d8c3828623fa1a2e2554f121d4c39a.jpg  \n",
            " extracting: train/batch-6-077040_jpg.rf.0a0648f8639f31c618ebe561e923073f.jpg  \n",
            " extracting: train/batch-6-077070_jpg.rf.c91b19f05efcb2a22aa157369c782caa.jpg  \n",
            " extracting: train/batch-6-077100_jpg.rf.118699caa6e41e2d3a10865f028feec8.jpg  \n",
            " extracting: train/batch-6-077130_jpg.rf.1d9d87aaf10536906d15397c0fa966bf.jpg  \n",
            " extracting: train/batch-6-077190_jpg.rf.efd21bf49d726ccaa48f884c2c6b3dac.jpg  \n",
            " extracting: train/batch-6-077220_jpg.rf.0cfc59d0ad7035e6ffc8cad1a50f3bb8.jpg  \n",
            " extracting: train/batch-6-077250_jpg.rf.72d09d5d0037a4e57a874c0762ddd3c7.jpg  \n",
            " extracting: train/batch-6-077280_jpg.rf.c605fac1a9bfd9194712a4493e14307b.jpg  \n",
            " extracting: train/batch-6-077340_jpg.rf.80bca1eb2dff530fb13b75a7c4d5bd59.jpg  \n",
            " extracting: train/batch-6-077370_jpg.rf.a0c3618092ea5249efb438e93e9852d8.jpg  \n",
            " extracting: train/batch-6-077460_jpg.rf.e4b432533333c4ceebffcd1fd9453cf7.jpg  \n",
            " extracting: train/batch-6-077490_jpg.rf.789d1fd671fe124ce2b7d4a070b57aa8.jpg  \n",
            " extracting: train/batch-6-077550_jpg.rf.245b3fcaf914b7b682cda0e2297617fd.jpg  \n",
            " extracting: train/batch-6-077580_jpg.rf.8bf296e2754baf5664d16d067ae8e66a.jpg  \n",
            " extracting: train/batch-6-077640_jpg.rf.9048e5a85e84f547fe0b3fa7b55c69cd.jpg  \n",
            " extracting: train/batch-6-077700_jpg.rf.40dddb3f7f5b8d1abad90cb28e485eef.jpg  \n",
            " extracting: train/batch-6-077730_jpg.rf.8e1fe8ef5cbc9d2cbfd12ea0f09bb7a7.jpg  \n",
            " extracting: train/batch-6-077790_jpg.rf.28765bc5b6bc054444425548557bf346.jpg  \n",
            " extracting: train/batch-6-077820_jpg.rf.0e9479200cab3403ef02eea9b595aba8.jpg  \n",
            " extracting: train/batch-6-077970_jpg.rf.9a1913a9d1b7ff825291af2a7ecb978c.jpg  \n",
            " extracting: train/batch-6-078000_jpg.rf.b89d8086d77bf347fa72f0285b1836fc.jpg  \n",
            " extracting: train/batch-6-078060_jpg.rf.0d2dfdcc0010d8eda098c93b0cb84a68.jpg  \n",
            " extracting: train/batch-6-078090_jpg.rf.2440b00356ef5ed7dc33982c7ba42f0d.jpg  \n",
            " extracting: train/batch-6-078120_jpg.rf.2f6f1133ad0d0a37751f76e4efa359d2.jpg  \n",
            " extracting: train/batch-6-078150_jpg.rf.9281884d143a78ffef0624f62594ea9a.jpg  \n",
            " extracting: train/batch-6-078240_jpg.rf.fbcabc94c9e60898178e4fe11baf4249.jpg  \n",
            " extracting: train/batch-6-078270_jpg.rf.71538ba43cd7a7d8ad29375b1a076f0c.jpg  \n",
            " extracting: train/batch-6-078300_jpg.rf.02cbe1cf45a91b9ddebfa519425943c7.jpg  \n",
            " extracting: train/batch-6-078330_jpg.rf.fdd4ddf3c2db0cd577615448b49f961b.jpg  \n",
            " extracting: train/batch-6-078360_jpg.rf.eedbf834cd90dbb9c39779e70c9fd86d.jpg  \n",
            " extracting: train/batch-6-078450_jpg.rf.74f9bfdf486a37941830dfaacbbc949b.jpg  \n",
            " extracting: train/batch-6-078480_jpg.rf.e2beb5d3a53a9c482848953d25af22ba.jpg  \n",
            " extracting: train/batch-6-078510_jpg.rf.e30f230dd061b47b277c21feeb471192.jpg  \n",
            " extracting: train/batch-6-078570_jpg.rf.60742049491a47d2d94e3bc988a07a0d.jpg  \n",
            " extracting: train/batch-6-078600_jpg.rf.23c8c0b860074a3367be4b7ee74fb0db.jpg  \n",
            " extracting: train/batch-6-078630_jpg.rf.498956af2668d14d94c9696a42787cfd.jpg  \n",
            " extracting: train/batch-6-078660_jpg.rf.d507e63e447e7e8c06bb0237e2d097a9.jpg  \n",
            " extracting: train/batch-6-078690_jpg.rf.166ccdec4e50ef4f9dbcb53d92fbebb9.jpg  \n",
            " extracting: train/batch-6-078720_jpg.rf.3c7941aae8fa6f7184857277367130c3.jpg  \n",
            " extracting: train/batch-6-078750_jpg.rf.03553b44e7c7d3b246b2692021ab171a.jpg  \n",
            " extracting: train/batch-6-078780_jpg.rf.b3bbc6e8a20b7073c4715830b7f2fc71.jpg  \n",
            " extracting: train/batch-6-079560_jpg.rf.3137b595d33bb9a38e3a5bafdfa0faba.jpg  \n",
            " extracting: train/batch-6-079590_jpg.rf.c37f6e0bffccbd2dd76dc3a38ffa9459.jpg  \n",
            " extracting: train/batch-6-079620_jpg.rf.75b06df9c7b280cdc8e385a1ad986922.jpg  \n",
            " extracting: train/batch-6-080400_jpg.rf.b36bd778c4984a364ec6b5cd07fa4035.jpg  \n",
            " extracting: train/batch-6-080430_jpg.rf.d16ab8273208f758fadc4452c2265b42.jpg  \n",
            " extracting: train/batch-6-080490_jpg.rf.20b0a0b378af019aa6a4ccb678ec00b7.jpg  \n",
            " extracting: train/batch-6-080520_jpg.rf.ccd18ec3c0101ac6ccd8d09b3ed6962a.jpg  \n",
            " extracting: train/batch-6-080550_jpg.rf.ee150d5aa83509a22e537d9706b91891.jpg  \n",
            " extracting: train/batch-6-080610_jpg.rf.97ec292281ac33ddd3e946da731dc1c3.jpg  \n",
            " extracting: train/batch-6-080640_jpg.rf.060ac1b4b41dae7f00527df4ae10d07c.jpg  \n",
            " extracting: train/batch-6-080670_jpg.rf.a061c389c03c633ebb6eaca9d4bf9ef4.jpg  \n",
            " extracting: train/batch-6-081210_jpg.rf.3432ee89f3e53c9c5f7261e2c5990a14.jpg  \n",
            " extracting: train/batch-6-081330_jpg.rf.64c4a65cbea7a0521393b4702d57c142.jpg  \n",
            " extracting: train/batch-6-081450_jpg.rf.463e7da98d37ee8f35ef938e164eadc4.jpg  \n",
            " extracting: train/batch-6-081480_jpg.rf.0d7ac617be64cd2ad43bd9252d75e400.jpg  \n",
            " extracting: train/batch-6-081510_jpg.rf.5ae5f3f90c2b878c44f0f7d4dbae0aa7.jpg  \n",
            " extracting: train/batch-6-081570_jpg.rf.627b72ad049a36d8a9b8a0a0d73f8f50.jpg  \n",
            " extracting: train/batch-6-081600_jpg.rf.fbde67cb3b5b7e6aeb335350f800ae38.jpg  \n",
            " extracting: train/batch-6-081630_jpg.rf.685677decb9f8b7f2caa2d5b3a12c7f7.jpg  \n",
            " extracting: train/batch-6-081660_jpg.rf.defe2693bd760dc21f6c97d46cb9de45.jpg  \n",
            " extracting: train/batch-6-081690_jpg.rf.7692c0e3c4c71421d1d077f689c12f35.jpg  \n",
            " extracting: train/batch-6-081750_jpg.rf.35fa9b4353860b8feb647174ea03a1c3.jpg  \n",
            " extracting: train/batch-6-081810_jpg.rf.7e9aada64776f6179414d2eefbd5d289.jpg  \n",
            " extracting: train/batch-6-081840_jpg.rf.c6c001e77d167973edf8794c87ad4ddb.jpg  \n",
            " extracting: train/batch-6-081900_jpg.rf.964d9c985e14401a2e03370695d5713d.jpg  \n",
            " extracting: train/batch-6-081930_jpg.rf.a9a14575ed9717d12774875b4dd88c3a.jpg  \n",
            " extracting: train/batch-6-081990_jpg.rf.36cb88a8f9e2fde0bf7c77e8b05a15bb.jpg  \n",
            " extracting: train/batch-6-082110_jpg.rf.7f7f5cb294580512b4e54c6c929cf164.jpg  \n",
            " extracting: train/batch-6-082140_jpg.rf.633b116a9dc77220c77b39c0d280446c.jpg  \n",
            " extracting: train/batch-6-082200_jpg.rf.160708829652e85cb4d25f502c317ce8.jpg  \n",
            " extracting: train/batch-6-082230_jpg.rf.d2ab214f674583282d5e958d6da47c9d.jpg  \n",
            " extracting: train/batch-6-082260_jpg.rf.989b04a223350b2422d9af04ea7c5406.jpg  \n",
            " extracting: train/batch-6-082290_jpg.rf.e202ac87e60620112c7c5c8df3e3815d.jpg  \n",
            " extracting: train/batch-6-082350_jpg.rf.26550ab7ffc90358627a22ec94492474.jpg  \n",
            " extracting: train/batch-6-082620_jpg.rf.d408f3173ba46fb84f2d6f63216d3461.jpg  \n",
            " extracting: train/batch-6-082650_jpg.rf.1f11de8e93647c85fdf1461a73f5a2ff.jpg  \n",
            " extracting: train/batch-6-082680_jpg.rf.a6bda7d3cbed06896f28fdbd95c22b6d.jpg  \n",
            " extracting: train/batch-6-082740_jpg.rf.fb7f1d7403485473836d63585f5b8f31.jpg  \n",
            " extracting: train/batch-6-082800_jpg.rf.de908835f537a05ce5cc68b26bc9c0b9.jpg  \n",
            " extracting: train/batch-6-082860_jpg.rf.e207dbea78eda9e277f961cd972b6e51.jpg  \n",
            " extracting: train/batch-6-082950_jpg.rf.6d9391d808a5dd551085a9bff35e91db.jpg  \n",
            " extracting: train/batch-6-082980_jpg.rf.d5324c717a1e97bcff73c40b5d6eb485.jpg  \n",
            " extracting: train/batch-6-083010_jpg.rf.f11d4b2058386d84ec4424e613e94a31.jpg  \n",
            " extracting: train/batch-6-083040_jpg.rf.dcf41e139fb940579decfb1dc28d199c.jpg  \n",
            " extracting: train/batch-6-083070_jpg.rf.4020548f714a2e4619bcb30ad87109c3.jpg  \n",
            " extracting: train/batch-6-083880_jpg.rf.399eed74bcc218401fe8a3c1f0d9041a.jpg  \n",
            " extracting: train/batch-6-083910_jpg.rf.2de568ebdee114397ab43e61b872927c.jpg  \n",
            " extracting: train/batch-6-084180_jpg.rf.919e866f1bf87b65256c178926f6800a.jpg  \n",
            " extracting: train/batch-6-084210_jpg.rf.dce67d4b8816e52751f9ee08f77c6997.jpg  \n",
            " extracting: train/batch-6-084240_jpg.rf.69ce70eb16049aee5a803764fce179a9.jpg  \n",
            " extracting: train/batch-6-084270_jpg.rf.cee12fbebb5cc3fe1d2d51c0fcae75b2.jpg  \n",
            " extracting: train/batch-6-084300_jpg.rf.e04bebc9a97e529926e5d96463bdbc86.jpg  \n",
            " extracting: train/batch-6-084360_jpg.rf.eb210047f1d6c6e1b9d3188d4108f003.jpg  \n",
            " extracting: train/batch-6-084450_jpg.rf.02e5956f2d2c81d739ce36887505feac.jpg  \n",
            " extracting: train/batch-6-084480_jpg.rf.7601dee23a275987c0d17480b0f6671f.jpg  \n",
            " extracting: train/batch-6-084510_jpg.rf.ab59843039a7260e821a40cff2cd7f7d.jpg  \n",
            " extracting: train/batch-6-084660_jpg.rf.c89c254724b099d1075a8a8ada43e381.jpg  \n",
            " extracting: train/batch-6-084690_jpg.rf.838289748ba14e57584683348320b887.jpg  \n",
            " extracting: train/batch-6-084750_jpg.rf.11673dad5f786b0011832ff00749dcea.jpg  \n",
            " extracting: train/batch-6-084780_jpg.rf.b49a4c671fbe553165ae1dfb25fb4bdb.jpg  \n",
            " extracting: train/batch-6-084810_jpg.rf.46046a4b68ac0c1c7d92d0b8dbed07ba.jpg  \n",
            " extracting: train/batch-6-084840_jpg.rf.fc8391487679e46ae116b8886ed94646.jpg  \n",
            " extracting: train/batch-6-084870_jpg.rf.eef42da978d4d98a90da889aba4cd481.jpg  \n",
            " extracting: train/batch-6-084930_jpg.rf.ef9403fc005bfcb2f51361061fdc51d2.jpg  \n",
            " extracting: train/batch-6-084960_jpg.rf.bdfcbb3f6779b2aececd3f92dccd0ad6.jpg  \n",
            " extracting: train/batch-6-084990_jpg.rf.d18c60c60201263212fd27646b6f1749.jpg  \n",
            " extracting: train/batch-6-085050_jpg.rf.542d7e973377715343e97a3b1cd2b2e2.jpg  \n",
            " extracting: train/batch-6-085080_jpg.rf.272ed4372e3188c6d4049e9b9ff03f28.jpg  \n",
            " extracting: train/batch-6-085110_jpg.rf.fb597f126d0211eb45ddf2066cdf4087.jpg  \n",
            " extracting: train/batch-6-085140_jpg.rf.a474fd0e0945c493b82674ab5511c4d9.jpg  \n",
            " extracting: train/batch-6-085320_jpg.rf.f09937add150e2c286f2f66836737618.jpg  \n",
            " extracting: train/batch-6-085350_jpg.rf.7d664de6a8c1c861f9986ae6a62d7c22.jpg  \n",
            " extracting: train/batch-6-085380_jpg.rf.f1736ad8e58cb3a165ba11bcc2af8a7d.jpg  \n",
            " extracting: train/batch-6-085410_jpg.rf.afa6b02c8e431045b244fd313158ecfe.jpg  \n",
            " extracting: train/batch-6-085440_jpg.rf.fe17837a3e538b37471b1e61681be5cb.jpg  \n",
            " extracting: train/batch-6-085470_jpg.rf.81631b7efd21b9565e7a7f99b356811e.jpg  \n",
            " extracting: train/batch-6-085500_jpg.rf.a7ccca6ca70e530a7cc1383f48fcca07.jpg  \n",
            " extracting: train/batch-6-085530_jpg.rf.592b8c2e119a1f80593706359cde967c.jpg  \n",
            " extracting: train/batch-6-085590_jpg.rf.bb61b0e2248aca096660fd8fe2c1bd7b.jpg  \n",
            " extracting: train/batch-6-085650_jpg.rf.5c8a9f648044da0b5222053ce91ab1e7.jpg  \n",
            " extracting: train/batch-6-085680_jpg.rf.83b98f4ad690aab6e2ece08e74132995.jpg  \n",
            " extracting: train/batch-6-085710_jpg.rf.8eedea8ee267466543e9f07265a34782.jpg  \n",
            " extracting: train/batch-6-085830_jpg.rf.6d5bc1692248168ba2df3938b8deced6.jpg  \n",
            " extracting: train/batch-6-085890_jpg.rf.7d4fbcdc3b99348e925a4d1629076b9f.jpg  \n",
            " extracting: train/batch-6-085920_jpg.rf.272ca47a48e11df96e8a452ffb889686.jpg  \n",
            " extracting: train/batch-6-085950_jpg.rf.f583ec142a30b5cd6b8cc26395376855.jpg  \n",
            " extracting: train/batch-6-086010_jpg.rf.1cacdc66a2848a0c288655a4c62f27a9.jpg  \n",
            " extracting: train/batch-6-086040_jpg.rf.955b7997378f9ea320ce821b11737458.jpg  \n",
            " extracting: train/batch-6-086070_jpg.rf.b31261fc8df5570514f16ba72a5b3332.jpg  \n",
            " extracting: train/batch-6-086100_jpg.rf.d6b5a272ed5a7074c5817f49283510ad.jpg  \n",
            " extracting: train/batch-6-086130_jpg.rf.e6d9326ad1f4c395ee8ca321498f9404.jpg  \n",
            " extracting: train/batch-6-086190_jpg.rf.f213e62d19434bcace4fd52691e9b495.jpg  \n",
            " extracting: train/batch-6-086220_jpg.rf.733112a2f912746cbdeadb65ce2393ce.jpg  \n",
            " extracting: train/batch-6-086280_jpg.rf.114f66017ef292a363f8729dbbf6420a.jpg  \n",
            " extracting: train/batch-6-086310_jpg.rf.4f6a8965789303dea0346fe7e986b675.jpg  \n",
            " extracting: train/batch-6-086340_jpg.rf.ae1c9913df69d6af70d530dd84c3e9cb.jpg  \n",
            " extracting: train/batch-6-086370_jpg.rf.6ee66999816988f1e1b06f4a95af614d.jpg  \n",
            " extracting: train/batch-6-086430_jpg.rf.75a9c8749f1542fe0dcf24fee062c123.jpg  \n",
            " extracting: train/batch-6-086460_jpg.rf.1ebc97acf57b9a22ef1d8886547682c8.jpg  \n",
            " extracting: train/batch-6-086520_jpg.rf.905b1b2ae462ef96f4ade222498d61ab.jpg  \n",
            " extracting: train/batch-6-086580_jpg.rf.ae0d113b91a83ea93b4d8954bd957f2e.jpg  \n",
            " extracting: train/batch-6-086610_jpg.rf.3d71d7f742fe0e91efb9e26f4caaf405.jpg  \n",
            " extracting: train/batch-6-086640_jpg.rf.31c737d005c84b9cf37d161f34bfbc98.jpg  \n",
            " extracting: train/batch-6-086670_jpg.rf.2dc9edfacf12dbbc47862009a70957ab.jpg  \n",
            " extracting: train/batch-6-086730_jpg.rf.35ac9515b98fa88910a4c8903b4a67eb.jpg  \n",
            " extracting: train/batch-6-086760_jpg.rf.c16403e50e6c1d5d168ccac16abf3cab.jpg  \n",
            " extracting: train/batch-6-086820_jpg.rf.a26dda1de31d774aad509ced53b1b9f8.jpg  \n",
            " extracting: train/batch-6-086850_jpg.rf.0bbb0b2a4d9fb94bdaf23bb5200554a6.jpg  \n",
            " extracting: train/batch-6-086880_jpg.rf.fe16599bfa1e31290f22dd03b22cc7b8.jpg  \n",
            " extracting: train/batch-6-086910_jpg.rf.6ba31e674b6a42deba7856e51247bf55.jpg  \n",
            " extracting: train/batch-6-086940_jpg.rf.f4351b0e2bbfcb126f7a6bc4226afb3b.jpg  \n",
            " extracting: train/batch-6-086970_jpg.rf.6a7c5c50c6f2fb7b1d297270121bb16d.jpg  \n",
            " extracting: train/batch-6-087000_jpg.rf.653b9eefd664078fff42dc180ef3ab75.jpg  \n",
            " extracting: train/batch-6-087030_jpg.rf.d40176750f7c350ad5576e4c58d7d864.jpg  \n",
            " extracting: train/batch-6-087090_jpg.rf.432114e29199c5f2120601039b8532fd.jpg  \n",
            " extracting: train/batch-6-087120_jpg.rf.8eea1102da25be6c22c98f1d606e4f3e.jpg  \n",
            " extracting: train/batch-6-087150_jpg.rf.b1970c9dd36bf36dbf593f487f35762a.jpg  \n",
            " extracting: train/batch-6-087180_jpg.rf.6989a020fd4f164f2f3992f637b70088.jpg  \n",
            " extracting: train/batch-6-087210_jpg.rf.3a48c4e12c709512c157d6a4a03aa588.jpg  \n",
            " extracting: train/batch-6-087270_jpg.rf.fa4b756f1dc71077ae0b45539ad260de.jpg  \n",
            " extracting: train/batch-6-087300_jpg.rf.7cf98ffe551d9dc9a1cf137a8d84872d.jpg  \n",
            " extracting: train/batch-6-087570_jpg.rf.b433273daa51bb7f8de985dca6de8ba9.jpg  \n",
            " extracting: train/batch-6-087600_jpg.rf.4c2713f1488036a0ce6eacaf3cdc7e81.jpg  \n",
            " extracting: train/batch-6-087690_jpg.rf.a6f6fb366de6933cc36b216ec27c0772.jpg  \n",
            " extracting: train/batch-6-087900_jpg.rf.6ac7eba3cd0c7e200a3934e49b779d62.jpg  \n",
            " extracting: train/batch-6-087990_jpg.rf.4b3885dffbd1906e3be37cb298df97ea.jpg  \n",
            " extracting: train/batch-6-088050_jpg.rf.719ef279513a1d5960a1d48541d40907.jpg  \n",
            " extracting: train/batch-6-088080_jpg.rf.6bfb1ddaada4bd2aa2a9e0412033a284.jpg  \n",
            " extracting: train/batch-6-088110_jpg.rf.901adb92cddc90f063878b246b5cdd58.jpg  \n",
            " extracting: train/batch-6-088140_jpg.rf.86fd879856160b680410de3aa8e139dc.jpg  \n",
            " extracting: train/batch-6-088230_jpg.rf.462ab47e5e633b0848f72ca5cde5f2f5.jpg  \n",
            " extracting: train/batch-6-088650_jpg.rf.3507c884b4c631a84cc3900276ce1f92.jpg  \n",
            " extracting: train/batch-6-088800_jpg.rf.51fbb6ef8f5290523ad453379960cf7d.jpg  \n",
            " extracting: train/batch-6-088860_jpg.rf.2223bf8349393a3bda396365c2088f32.jpg  \n",
            " extracting: train/batch-6-088950_jpg.rf.bd40c5b73ebdc1db86681215034e1800.jpg  \n",
            " extracting: train/batch-6-089010_jpg.rf.e050044e730d363af83ca4e803fb9d22.jpg  \n",
            " extracting: train/batch-6-089070_jpg.rf.edfd2ebee0174e045bd23ccefc541e90.jpg  \n",
            " extracting: train/batch-6-089100_jpg.rf.78b5d983c2a1fc334aff4c36d99f64bc.jpg  \n",
            " extracting: train/batch-6-089130_jpg.rf.3a5a43b7a7a7f50287b55f6cbff90b25.jpg  \n",
            " extracting: train/batch-6-089160_jpg.rf.909fde9963bcf8821507556f34103d75.jpg  \n",
            " extracting: train/batch-6-089190_jpg.rf.390b5b4f0f09e6f781e1f55d291a4987.jpg  \n",
            " extracting: train/batch-6-089250_jpg.rf.932d405098c69c9a570c1c5dec88e387.jpg  \n",
            " extracting: train/batch-6-089280_jpg.rf.b6765d4006a881e7653810c1834e6b5d.jpg  \n",
            " extracting: train/batch-6-089310_jpg.rf.91fedbff3bfd23460d48597a7127edf7.jpg  \n",
            " extracting: train/batch-6-089340_jpg.rf.867e26c2ee23e6e56cfea125c542d6b1.jpg  \n",
            " extracting: train/batch-6-089370_jpg.rf.7770af644c30b9355074699e53804230.jpg  \n",
            " extracting: train/batch-6-089400_jpg.rf.1d901227841f9e51b0239968bdbe6935.jpg  \n",
            " extracting: train/batch-6-089430_jpg.rf.e35032349fcc0c2fb72c09bf308a7f30.jpg  \n",
            " extracting: train/batch-6-089490_jpg.rf.88681766eb99418a69e66e994e6a7436.jpg  \n",
            " extracting: train/batch-6-089550_jpg.rf.f8d21b100f049561178b7f6e61321966.jpg  \n",
            " extracting: train/batch-6-089580_jpg.rf.8be125fd1f6d72ccfbd39b6dc758132e.jpg  \n",
            " extracting: train/batch-6-089640_jpg.rf.ce735079b95ad5147ad5fc68c248c650.jpg  \n",
            " extracting: train/batch-6-089700_jpg.rf.a007fac0e8d5cb628469b60e554d016f.jpg  \n",
            " extracting: train/batch-6-089730_jpg.rf.ebcd44726ed0093b16bbcc31e81cbc16.jpg  \n",
            " extracting: train/batch-6-089790_jpg.rf.024cc77677b7eaae7299d948a0514c19.jpg  \n",
            " extracting: train/batch-6-089820_jpg.rf.4976a2fecf917b70ec54d1d59878e1c7.jpg  \n",
            " extracting: train/batch-6-089850_jpg.rf.ac9a786827ce9c3c3f36273759d9b2a5.jpg  \n",
            " extracting: train/batch-6-089940_jpg.rf.7bb326e4ee2dc1b67012dfd313e63d6f.jpg  \n",
            " extracting: train/batch-6-089970_jpg.rf.b2e1dbaac0c4ccfb35dd8a1743b62e2f.jpg  \n",
            " extracting: train/batch-6-090030_jpg.rf.31fa3660ba47331a149a4d82d807f855.jpg  \n",
            " extracting: train/batch-6-090060_jpg.rf.6905be369c67c8236cc62caf7378424a.jpg  \n",
            " extracting: train/batch-6-090090_jpg.rf.df3538f0f83bdc2ed1ae7e2ac8c9fd87.jpg  \n",
            " extracting: train/batch-6-090150_jpg.rf.afc9185cf7f43737f5eae8146654c699.jpg  \n",
            " extracting: train/batch-6-090210_jpg.rf.15ca835bdcead07ac9da85c3a037e500.jpg  \n",
            " extracting: train/batch-6-090270_jpg.rf.922e40d4cd347ea2adea45f23cf08879.jpg  \n",
            " extracting: train/batch-6-090360_jpg.rf.0c76c8cf1de1c838589c479d155d8617.jpg  \n",
            " extracting: train/batch-6-090420_jpg.rf.d9f085275c1910f12fff32758b0a971f.jpg  \n",
            " extracting: train/batch-6-090450_jpg.rf.95d889649b6f21c9821debd947f49851.jpg  \n",
            " extracting: train/batch-6-090480_jpg.rf.b220dcfde61d397205bd885f6d677403.jpg  \n",
            " extracting: train/batch-6-090510_jpg.rf.c0b1c51c204a76ebc1e5d1ef0047fe8f.jpg  \n",
            " extracting: train/batch-6-090540_jpg.rf.2652984a08cbf95eb5e4ff2c9b7ac7d3.jpg  \n",
            " extracting: train/batch-6-090570_jpg.rf.55e905c96d91d5511e93632bb39c2190.jpg  \n",
            " extracting: train/batch-6-090600_jpg.rf.250bd18f0e723dcc4e9189612b1ba142.jpg  \n",
            " extracting: train/batch-6-090720_jpg.rf.55b22d60c215b0e3708788256e93b5af.jpg  \n",
            " extracting: train/batch-6-091170_jpg.rf.f2ce14a416086ed76608cd5a4e07af1f.jpg  \n",
            " extracting: train/batch-6-091200_jpg.rf.3b3247e3e9ab20fcddf2a3d42ac85e1d.jpg  \n",
            " extracting: train/batch-6-091320_jpg.rf.1a23ace1d40132bb3b744e2a8425c5dc.jpg  \n",
            " extracting: train/batch-6-091530_jpg.rf.0f924bc91b9a37dfc316e1ee473d9b56.jpg  \n",
            " extracting: train/batch-6-091560_jpg.rf.9b281cb95ca0ecf9f358ca5c18737b22.jpg  \n",
            " extracting: train/batch-6-091680_jpg.rf.534b41bcba6a612b4444387448ef0efc.jpg  \n",
            " extracting: train/batch-6-091710_jpg.rf.944e9bd04a20e12c762f8d529bc74fac.jpg  \n",
            " extracting: train/batch-6-091920_jpg.rf.bdc4859a1edd01a530ffda06898088f6.jpg  \n",
            " extracting: train/batch-6-091950_jpg.rf.1256c1ef418543aedcaa49b4ffaa1ed1.jpg  \n",
            " extracting: train/batch-6-091980_jpg.rf.ba95f7e78d9b611f6fdfe6fd546d56b6.jpg  \n",
            " extracting: train/batch-6-092010_jpg.rf.9b110511722c5f8ae94b3b78516754e5.jpg  \n",
            " extracting: train/batch-6-092370_jpg.rf.8e833309596e4df4e9aab5878589ff8f.jpg  \n",
            " extracting: train/batch-6-092520_jpg.rf.2c2b760e6c463368b4906545247b9c5c.jpg  \n",
            " extracting: train/batch-6-092550_jpg.rf.71f34a36fa83116848daf38ec9240775.jpg  \n",
            " extracting: train/batch-6-092580_jpg.rf.1bbe7d4a0a821d7559747e094d4080be.jpg  \n",
            " extracting: train/batch-6-092610_jpg.rf.f4cd39ffd0b1d3b1bf2835f4477071df.jpg  \n",
            " extracting: train/batch-6-092730_jpg.rf.fe28004ade5939ea9294d343a4c36ac7.jpg  \n",
            " extracting: train/batch-6-092760_jpg.rf.acfb2f28c9633571e298b01a887c2742.jpg  \n",
            " extracting: train/batch-6-092790_jpg.rf.cd296f3ca637d8f77accb3053e8d5422.jpg  \n",
            " extracting: train/batch-6-092820_jpg.rf.c3ae6e333ae6e88e8633db79a2c668e4.jpg  \n",
            " extracting: train/batch-6-092850_jpg.rf.a830a8815eda0c35cf2d7e35422ff2f7.jpg  \n",
            " extracting: train/batch-6-092880_jpg.rf.338b73c918b161e5a52f8c1a570b270b.jpg  \n",
            " extracting: train/batch-6-092940_jpg.rf.13c0f62d204a3842ba7f8927be827e64.jpg  \n",
            " extracting: train/batch-6-092970_jpg.rf.10579b44f0872f7e69eadb7ff061b960.jpg  \n",
            " extracting: train/batch-6-093060_jpg.rf.4cb8d2a0720e28d8469fc92292378940.jpg  \n",
            " extracting: train/batch-6-093150_jpg.rf.f0961c00135918a4ce886d2b07de3953.jpg  \n",
            " extracting: train/batch-6-093180_jpg.rf.bf681202778549416ad25807bd238040.jpg  \n",
            " extracting: train/batch-6-093240_jpg.rf.244dcf5f1afc1a5516a6a675404e0862.jpg  \n",
            " extracting: train/batch-6-093270_jpg.rf.a44c213baba4a9c9761abcfb88e179c6.jpg  \n",
            " extracting: train/batch-6-093330_jpg.rf.9f11815a3189b02a5c7eca938b726d0c.jpg  \n",
            " extracting: train/batch-6-093450_jpg.rf.6bf869010a072f14563eeba9debe88ea.jpg  \n",
            " extracting: train/batch-6-093870_jpg.rf.a93ac1ba22534c583fd98dc3a768ae74.jpg  \n",
            " extracting: train/batch-6-093900_jpg.rf.38ec5fdab29dd3283f9534fd2701b4cf.jpg  \n",
            " extracting: train/batch-6-094200_jpg.rf.66f93f1c811b875013d653ace28a3eaa.jpg  \n",
            " extracting: train/batch-6-094230_jpg.rf.91c13b990644d24d6c3bf0a152aaf42c.jpg  \n",
            " extracting: train/batch-6-094260_jpg.rf.93894deb822c330cb53ad1218539be78.jpg  \n",
            " extracting: train/batch-6-094290_jpg.rf.0b03ed0e01229a47172d33885425741e.jpg  \n",
            " extracting: train/batch-6-094320_jpg.rf.36e478d1a56c3c7d9bf3bef6793f4c9e.jpg  \n",
            " extracting: train/batch-6-094950_jpg.rf.4918006798621e291f68d5f8fd8fcfb4.jpg  \n",
            " extracting: train/batch-6-094980_jpg.rf.2b1a8f01a8901cedd1eb46bdb3170371.jpg  \n",
            " extracting: train/batch-6-095040_jpg.rf.fe1f97212ace4e1fe7414223e15ed66f.jpg  \n",
            " extracting: train/batch-6-095190_jpg.rf.840c3ae35919e793d15219cd954eabf2.jpg  \n",
            " extracting: train/batch-6-095220_jpg.rf.a33ff8eb0af7d9516a427008f9935172.jpg  \n",
            " extracting: train/batch-6-095250_jpg.rf.3a9010457e3bb46a14a585ce59b6f3f9.jpg  \n",
            " extracting: train/batch-6-095310_jpg.rf.95f68f789c0e9b5250b4c5783ca13515.jpg  \n",
            " extracting: train/batch-6-101820_jpg.rf.0eac6bc832364acc0e1c19d178b0ba8a.jpg  \n",
            " extracting: train/batch-6-101850_jpg.rf.787c2d4b3b21774969584dfb3cd853a8.jpg  \n",
            " extracting: train/batch-6-101880_jpg.rf.4bc7853d379b1b84dd747d3b35081318.jpg  \n",
            " extracting: train/batch-6-101970_jpg.rf.0c2e84b667e514557d4f751fda0bfa9f.jpg  \n",
            " extracting: train/batch-6-102000_jpg.rf.2c0c3a374b82a14d09bd537c0d79fa3a.jpg  \n",
            " extracting: train/batch-6-102030_jpg.rf.68f596ecdbb767ae4db68e5cdafb411b.jpg  \n",
            " extracting: train/batch-6-102060_jpg.rf.a2ae072171b5a4a520c6215296e9ffc5.jpg  \n",
            " extracting: train/batch-6-102090_jpg.rf.b32215619740cc863cba2c82f7192edc.jpg  \n",
            " extracting: train/batch-6-102120_jpg.rf.d77a9461497c2707bbccecc5453bce26.jpg  \n",
            " extracting: train/batch-6-102150_jpg.rf.099c8c300cb187860379b1e3bb512337.jpg  \n",
            " extracting: train/batch-6-102450_jpg.rf.e2ef8dd3e72f76d01d7612b6673c5266.jpg  \n",
            " extracting: train/batch-6-102480_jpg.rf.9a729a98f677834f9d767d09c96600d2.jpg  \n",
            " extracting: train/batch-6-102510_jpg.rf.e983b6fc53987f6d2477d6deb52ecf6d.jpg  \n",
            " extracting: train/batch-6-103590_jpg.rf.a4c802a7ef128da2df5f595d71f7c190.jpg  \n",
            " extracting: train/batch-6-103650_jpg.rf.6a27497972960a8a1ca1c2d9fed4cd5b.jpg  \n",
            " extracting: train/batch-6-103680_jpg.rf.7c59c9e3ec90092f5d59716d7eb50382.jpg  \n",
            " extracting: train/batch-6-103740_jpg.rf.931b1072e36b98eb911cccfcb443816d.jpg  \n",
            " extracting: train/batch-6-103800_jpg.rf.ded7608620304012f55f2eae82226ece.jpg  \n",
            " extracting: train/batch-6-103830_jpg.rf.ea18b2e753759d8989c3564fafba324a.jpg  \n",
            " extracting: train/batch-6-103860_jpg.rf.97fcb3893075d8f6c31eea0806c8daae.jpg  \n",
            " extracting: train/batch-6-103890_jpg.rf.3f0707bf7dc28f0b17717740e4979c34.jpg  \n",
            " extracting: train/batch-6-103920_jpg.rf.9ba114cd9025cf007059eb2d17987dad.jpg  \n",
            " extracting: train/batch-6-103950_jpg.rf.c65fe19533a9173011fc8aa5ee70e6c0.jpg  \n",
            " extracting: train/batch-6-103980_jpg.rf.4eb25d3b38f31a4f4cb1ac40e494145b.jpg  \n",
            " extracting: train/batch-6-104100_jpg.rf.aefc14a438dec96414a8cf69efaf96df.jpg  \n",
            " extracting: train/batch-6-104190_jpg.rf.34d7f020ba37394d7f074c2c4a7fb187.jpg  \n",
            " extracting: train/batch-6-104250_jpg.rf.0175efd875d22dbae3a547ff26b0c814.jpg  \n",
            " extracting: train/batch-6-104280_jpg.rf.b17381b2cb9948b38ffa5423f7922635.jpg  \n",
            " extracting: train/batch-6-104310_jpg.rf.7d2c333f8e7eea57c34617f717648db3.jpg  \n",
            " extracting: train/batch-6-104340_jpg.rf.f86fe6cee48764094e5d1ef68dde2f67.jpg  \n",
            " extracting: train/batch-6-104370_jpg.rf.c6378542f026f7fee0f08cdfafe104ea.jpg  \n",
            " extracting: train/batch-6-104400_jpg.rf.22da39dcfb98b58c2676439ee11ac4eb.jpg  \n",
            " extracting: train/batch-6-104430_jpg.rf.49ecbc3f5c899e728c495699a88f9cf0.jpg  \n",
            " extracting: train/batch-6-104460_jpg.rf.0e065ffe45e333bc0abac29f7df28873.jpg  \n",
            " extracting: train/batch-6-104490_jpg.rf.d9d9bbd426071751532233dd60fd265a.jpg  \n",
            " extracting: train/batch-6-104550_jpg.rf.9c59da415f3e94338313c09855b7bf95.jpg  \n",
            " extracting: train/batch-6-104580_jpg.rf.f3cf2804b600b524765238521acbbade.jpg  \n",
            " extracting: train/batch-6-104610_jpg.rf.d303586338a82af9f82b92432984dbe8.jpg  \n",
            " extracting: train/batch-6-104640_jpg.rf.706076440b45b8e538e2193d8f6754e7.jpg  \n",
            " extracting: train/batch-6-104670_jpg.rf.7e5321fd9ac404801d55a5b27fc7cb6d.jpg  \n",
            " extracting: train/batch-6-104730_jpg.rf.c5e812dfde50fd4580d3e2dfad4aa450.jpg  \n",
            " extracting: train/batch-6-104820_jpg.rf.f1dbf0d0159b69e783b2609039d15091.jpg  \n",
            " extracting: train/batch-6-104850_jpg.rf.2be0770ff77798385cd847522e83d5ce.jpg  \n",
            " extracting: train/batch-6-104910_jpg.rf.5aa0d1daf81b814e0a688be1119b58a9.jpg  \n",
            " extracting: train/batch-6-104970_jpg.rf.71e07b05180d94c54484b1b2724810a4.jpg  \n",
            " extracting: train/batch-6-105000_jpg.rf.47c5ade4ae3b77188ae289b9a257f90d.jpg  \n",
            " extracting: train/batch-6-105030_jpg.rf.970cf57b63ceaaa582c4cfcc59c0e119.jpg  \n",
            " extracting: train/batch-6-105090_jpg.rf.8dcae0bb455dc488934f1266aba7f991.jpg  \n",
            " extracting: train/batch-6-105120_jpg.rf.352e29a4f67305a9899b5981cc17e308.jpg  \n",
            " extracting: train/batch-6-105150_jpg.rf.cd29a07efd45aa6001a516c518ea97a3.jpg  \n",
            " extracting: train/batch-6-105240_jpg.rf.8eccb84496a1284b5c56f58a081bb0f7.jpg  \n",
            " extracting: train/batch-6-105270_jpg.rf.a6cf9c400ee2be12809d761907957b79.jpg  \n",
            " extracting: train/batch-6-105300_jpg.rf.dc6f5e3fe7558bea011d29fbc1556c85.jpg  \n",
            " extracting: train/batch-6-105330_jpg.rf.3bfef4e9f3c948d297db24a958cc4cc9.jpg  \n",
            " extracting: train/batch-6-105390_jpg.rf.6595a4810c750f8e2d50b8af5171fff4.jpg  \n",
            " extracting: train/batch-6-105420_jpg.rf.88d262a6e0ec0115b1a8ad977d78978f.jpg  \n",
            " extracting: train/batch-6-105450_jpg.rf.5ffa2cf3ae4cb76109f859b153f9009a.jpg  \n",
            " extracting: train/batch-6-105480_jpg.rf.af610d5548acb85b3d897ed66dda1dec.jpg  \n",
            " extracting: train/batch-6-105540_jpg.rf.2201fb4aa53dd9d306eb100f43f210ca.jpg  \n",
            " extracting: train/batch-6-105570_jpg.rf.b28f24baa4cf5ee1cb064c32c060a164.jpg  \n",
            " extracting: train/batch-6-105630_jpg.rf.4fe67aeaf91938afa55393a6a68b1ee2.jpg  \n",
            " extracting: train/batch-6-105660_jpg.rf.dde808e83f8a4a0ff86e8d52676832bd.jpg  \n",
            " extracting: train/batch-6-105690_jpg.rf.e0682b25aa8f4575db03ae58bd9cdea2.jpg  \n",
            " extracting: train/batch-6-105720_jpg.rf.b248e26c5e5a4ee931b2d22eed22e3a7.jpg  \n",
            " extracting: train/batch-6-105780_jpg.rf.a1fe45748f7486905127e66d31e5113e.jpg  \n",
            " extracting: train/batch-6-105810_jpg.rf.ae8c2497f86efcb6bf02489f0ad6063e.jpg  \n",
            " extracting: train/batch-6-105840_jpg.rf.a0b18f0730585e10f7d71e6799d6ff9b.jpg  \n",
            " extracting: train/batch-6-105870_jpg.rf.3bb453efa4c9cdf3c7198c47fdda2732.jpg  \n",
            " extracting: train/batch-6-105900_jpg.rf.bb367650b2ef765e0ce6a4a6e3f016ae.jpg  \n",
            " extracting: train/batch-6-105960_jpg.rf.d54651db8bccf461df1081b30b07d717.jpg  \n",
            " extracting: train/batch-6-105990_jpg.rf.c293d33bcb4c273c2ee163d02178fb56.jpg  \n",
            " extracting: train/batch-6-106050_jpg.rf.cfe9cc97a52543367af28b4df2c3e674.jpg  \n",
            " extracting: train/batch-6-106080_jpg.rf.1247b0db8fee8e3ab0b0bc9e5d80ae82.jpg  \n",
            " extracting: train/batch-6-106200_jpg.rf.7527ac443a763ed90a1fe51dba3bc849.jpg  \n",
            " extracting: train/batch-6-106530_jpg.rf.53974667f26585f1eb288ee4f93f0d4c.jpg  \n",
            " extracting: train/batch-6-106620_jpg.rf.15a6b383e5066564ca95df6c8dfa2a8e.jpg  \n",
            " extracting: train/batch-6-106650_jpg.rf.5202be04d73dc94b0d559a557fe2976f.jpg  \n",
            " extracting: train/batch-6-106680_jpg.rf.7bad58d4ad4ec6f5cb3c7854ff28ffc3.jpg  \n",
            " extracting: train/batch-6-106890_jpg.rf.39e7fd15d55d7d58ccdc79fcff140fad.jpg  \n",
            " extracting: train/batch-6-106920_jpg.rf.d27a9629ae5b0648fd3c5675a617a58b.jpg  \n",
            " extracting: train/batch-6-106950_jpg.rf.ba0d840c75f392a056e7cfb6a7311b80.jpg  \n",
            " extracting: train/batch-6-107010_jpg.rf.9e4c947d18e7e7539ab2a2896955b397.jpg  \n",
            " extracting: train/batch-6-107040_jpg.rf.69f91e2d5d5e1c40ddda0669405eb284.jpg  \n",
            " extracting: train/batch-6-107100_jpg.rf.ddebacbc40fe99b1b36e89618956f421.jpg  \n",
            " extracting: train/batch-6-107220_jpg.rf.52ddf6b77ca75d29573d5acc83cc9305.jpg  \n",
            " extracting: train/batch-6-107250_jpg.rf.dc023e6e4da999a2d1a38ac812eeac7f.jpg  \n",
            " extracting: train/batch-6-107280_jpg.rf.dffd836c453b404de280e466797b5ed5.jpg  \n",
            " extracting: train/batch-6-107310_jpg.rf.e814a33d0583713e47f57735ba220727.jpg  \n",
            " extracting: train/batch-6-107340_jpg.rf.36f80c2343edf33875f74abf66a476e2.jpg  \n",
            " extracting: train/batch-6-107370_jpg.rf.4c5d8369faf35418bd583cce43f63c9c.jpg  \n",
            " extracting: train/batch-6-107400_jpg.rf.e2950ece873e1d3623d537edfd4208ad.jpg  \n",
            " extracting: train/batch-6-107430_jpg.rf.1e8d441ef5314afc1cf8167b76d80d52.jpg  \n",
            " extracting: train/batch-6-107460_jpg.rf.34a05fa43bf8e63d4723ce9f8c14dec8.jpg  \n",
            " extracting: train/batch-6-107490_jpg.rf.4ec47478fd7b652ad17984a647fe25e9.jpg  \n",
            " extracting: train/batch-6-107520_jpg.rf.123c0e2e664d20f3b14dfe7e87e37d69.jpg  \n",
            " extracting: train/batch-6-107610_jpg.rf.6d300bcaed7704344df65c5106bab6e5.jpg  \n",
            " extracting: train/batch-6-107670_jpg.rf.c3149ad274afe8b1e8cf4a5cb202819d.jpg  \n",
            " extracting: train/batch-6-107790_jpg.rf.0570301bd5857139e0f4b042bea5e181.jpg  \n",
            " extracting: train/batch-6-107850_jpg.rf.db54173d565001f46ce14f06230cf4ce.jpg  \n",
            " extracting: train/batch-6-107910_jpg.rf.5a95bf4d294ddb4a7a5ddb7d19c6e91b.jpg  \n",
            " extracting: train/batch-6-107940_jpg.rf.ab29461c5aebf09296104b769466f48e.jpg  \n",
            " extracting: train/batch-6-107970_jpg.rf.ce9d9a9db3a6c620366d368c65ac3421.jpg  \n",
            " extracting: train/batch-6-108000_jpg.rf.347cee688e1f071efc5e17bc8ff6bceb.jpg  \n",
            " extracting: train/batch-6-108030_jpg.rf.9b4128036bc9dce8e688cc811ae0b2f6.jpg  \n",
            " extracting: train/batch-6-108060_jpg.rf.81d14a4af7498e3774b1cf31deb1a162.jpg  \n",
            " extracting: train/batch-6-108300_jpg.rf.0100ee22ec7187a20d8ff26c506e1784.jpg  \n",
            " extracting: train/batch-6-108360_jpg.rf.a992c9037387581a02099be50241b988.jpg  \n",
            " extracting: train/batch-6-108420_jpg.rf.d3becbe2407fe14f0a34f163d7eac6ff.jpg  \n",
            " extracting: train/batch-6-108450_jpg.rf.fef09791741c3586e9778b0d2cac7ff3.jpg  \n",
            " extracting: train/batch-6-108480_jpg.rf.d9d9eb1fe4d603d8ac516da5d072c499.jpg  \n",
            " extracting: train/batch-6-108540_jpg.rf.51021588c64c3e6b38892b79b7fafd0d.jpg  \n",
            " extracting: train/batch-6-108570_jpg.rf.0c2c9ecc5b7e12051cfc7539ffc5c7dd.jpg  \n",
            " extracting: train/batch-6-108630_jpg.rf.ac6ce7d51093cfcf6444ff1603e39ca8.jpg  \n",
            " extracting: train/batch-6-108660_jpg.rf.220985486d5910fcc1b4912dd6275652.jpg  \n",
            " extracting: train/batch-6-109650_jpg.rf.5ebc5b09baba8bbd6cd4bc6cd8269647.jpg  \n",
            " extracting: train/batch-6-109680_jpg.rf.98738525fc6e5b769c5ee72ca5d72133.jpg  \n",
            " extracting: train/batch-6-109710_jpg.rf.e83a111661678a58ba6e7e0e363edcfe.jpg  \n",
            " extracting: train/batch-6-109740_jpg.rf.454edd34b395d92ca54102be30abe45a.jpg  \n",
            " extracting: train/batch-6-109770_jpg.rf.929d246a7d2ee980b16bb83aaf6fae6f.jpg  \n",
            " extracting: train/batch-6-109860_jpg.rf.66d2ba08aa493659061ab812dea1724e.jpg  \n",
            " extracting: train/batch-6-109890_jpg.rf.152c2dfbd9378903ef7ecb42e852233f.jpg  \n",
            " extracting: train/batch-6-109950_jpg.rf.61c035ec48dd2699016a159fd07a91c8.jpg  \n",
            "   creating: valid/\n",
            " extracting: valid/_annotations.txt  \n",
            " extracting: valid/_classes.txt      \n",
            " extracting: valid/batch-6-052440_jpg.rf.18c6d7aa1c2cb084abe2d846b68b8296.jpg  \n",
            " extracting: valid/batch-6-053130_jpg.rf.a87009a6a01d8507a9f9a884c35e5fe4.jpg  \n",
            " extracting: valid/batch-6-053400_jpg.rf.04b0eb70b07b2192a66990e98c1f356a.jpg  \n",
            " extracting: valid/batch-6-053490_jpg.rf.706039d23451d9841a17208b0a8691ba.jpg  \n",
            " extracting: valid/batch-6-053520_jpg.rf.a110d19d703015c96056b9ab9aeb9130.jpg  \n",
            " extracting: valid/batch-6-053580_jpg.rf.5852ae9a452ea5f46897199f4eb9e4aa.jpg  \n",
            " extracting: valid/batch-6-054000_jpg.rf.24fb406fd0708a23077080813f6e672e.jpg  \n",
            " extracting: valid/batch-6-059820_jpg.rf.7b8971d3192f94d888a5f213d16aff23.jpg  \n",
            " extracting: valid/batch-6-061440_jpg.rf.c0e93c531eac4de3dd996a06802a839a.jpg  \n",
            " extracting: valid/batch-6-061740_jpg.rf.5d171d72bb32be58ba2e1d4c4fe6d01a.jpg  \n",
            " extracting: valid/batch-6-061800_jpg.rf.e6ceadda8a842220c1864fa120d0f8c4.jpg  \n",
            " extracting: valid/batch-6-061830_jpg.rf.b9cc55ca9f980d02bba26d4b85555f6b.jpg  \n",
            " extracting: valid/batch-6-061950_jpg.rf.f7a93c627e8193731271b0b336c738b8.jpg  \n",
            " extracting: valid/batch-6-062370_jpg.rf.6d27f3122187e29687e50011c43835f6.jpg  \n",
            " extracting: valid/batch-6-062700_jpg.rf.eec472211173703bba42fb221b441da7.jpg  \n",
            " extracting: valid/batch-6-062760_jpg.rf.5b28cfb6106decfe0497e3e8e69c6df4.jpg  \n",
            " extracting: valid/batch-6-062970_jpg.rf.1eaeaf1990595380c4db9a5d82344bdd.jpg  \n",
            " extracting: valid/batch-6-063150_jpg.rf.1d570acc42b69a8bd9e3a1af594737b6.jpg  \n",
            " extracting: valid/batch-6-063210_jpg.rf.e79379c3980dde9906246982532a269e.jpg  \n",
            " extracting: valid/batch-6-063540_jpg.rf.0139664bd2c41f4bd71a2dfec77426f9.jpg  \n",
            " extracting: valid/batch-6-063600_jpg.rf.f20c5bda06047b78f1fa69444f110204.jpg  \n",
            " extracting: valid/batch-6-063750_jpg.rf.dad19ed4c83904eaf37d20d82e3e5d71.jpg  \n",
            " extracting: valid/batch-6-063780_jpg.rf.098dc20ccb6838a7a03272eeb4e06e54.jpg  \n",
            " extracting: valid/batch-6-063870_jpg.rf.ed5f8c686efc7f1af4fc94d8476ad262.jpg  \n",
            " extracting: valid/batch-6-064020_jpg.rf.f9d25da9c67000bf4ff808c45bcad78d.jpg  \n",
            " extracting: valid/batch-6-064140_jpg.rf.3cda8f1a42d1ae087618c4b2d3d105f5.jpg  \n",
            " extracting: valid/batch-6-064530_jpg.rf.0ad7f45019174dac49d5a6ca5a53f95f.jpg  \n",
            " extracting: valid/batch-6-064800_jpg.rf.d4b57e7f40f8cc4de1ea209e55c54923.jpg  \n",
            " extracting: valid/batch-6-065310_jpg.rf.f89bbeebc9a88738c4543a95553cf802.jpg  \n",
            " extracting: valid/batch-6-065340_jpg.rf.3f2e08b75170e814fbd51debca8f9744.jpg  \n",
            " extracting: valid/batch-6-065370_jpg.rf.01733ba19bd535066466eb9eed994326.jpg  \n",
            " extracting: valid/batch-6-065790_jpg.rf.7d369ecdf94a25da7dbdef09c8132238.jpg  \n",
            " extracting: valid/batch-6-065820_jpg.rf.23a059fb9b3073855f495e6aa5cca516.jpg  \n",
            " extracting: valid/batch-6-065940_jpg.rf.5ce65e5cbd69546824265213171890c9.jpg  \n",
            " extracting: valid/batch-6-066030_jpg.rf.29b4f1d4944eeb1d6410ceb9a993ff60.jpg  \n",
            " extracting: valid/batch-6-066300_jpg.rf.1ee8502307c497e1d6029014d447bacd.jpg  \n",
            " extracting: valid/batch-6-066330_jpg.rf.a1dbbef6a740bc771d50b934856e72fd.jpg  \n",
            " extracting: valid/batch-6-066360_jpg.rf.48fdddedcd2f618f2c173f35c662a1da.jpg  \n",
            " extracting: valid/batch-6-066450_jpg.rf.5fef97dcec7ac08bc8bd9ac0657a0136.jpg  \n",
            " extracting: valid/batch-6-066630_jpg.rf.dbe41ea3aba4c04c93c29e1b2691cb74.jpg  \n",
            " extracting: valid/batch-6-066660_jpg.rf.98ff9607e24b93da25dd236fc3665f17.jpg  \n",
            " extracting: valid/batch-6-066750_jpg.rf.01cf2ac196fa8ddae4803a013fc257c0.jpg  \n",
            " extracting: valid/batch-6-066900_jpg.rf.2303980f4e3c85f489ad7f42d67e2a50.jpg  \n",
            " extracting: valid/batch-6-067080_jpg.rf.d1f4dfec0eda05bfca28dea8183edafa.jpg  \n",
            " extracting: valid/batch-6-067260_jpg.rf.64e2a80c85779c0d2c4108107802b9ff.jpg  \n",
            " extracting: valid/batch-6-068310_jpg.rf.70692ef36d7c690a809ff07d96df0bb9.jpg  \n",
            " extracting: valid/batch-6-068640_jpg.rf.7f94ef32a197fde5a5ebb3aec29a02c9.jpg  \n",
            " extracting: valid/batch-6-068670_jpg.rf.0491cf85ff42ec3b3647a4d43ca5c818.jpg  \n",
            " extracting: valid/batch-6-068820_jpg.rf.740cdb6da9cb7c1c3ffb3c8005f44682.jpg  \n",
            " extracting: valid/batch-6-069090_jpg.rf.c55cb5b4600952969827ce35412e3a3e.jpg  \n",
            " extracting: valid/batch-6-070620_jpg.rf.3b41bdb83a14d4cd9e20ac52913215e9.jpg  \n",
            " extracting: valid/batch-6-072840_jpg.rf.d070bf752b8273e231332338a6a8197f.jpg  \n",
            " extracting: valid/batch-6-073650_jpg.rf.8cf3b988ef7d983d897048475eefa63d.jpg  \n",
            " extracting: valid/batch-6-073860_jpg.rf.3f71e98c17294fe4d9728f48f52201cd.jpg  \n",
            " extracting: valid/batch-6-075030_jpg.rf.aa6bb5cae24533ab680f3ac024cc18db.jpg  \n",
            " extracting: valid/batch-6-075300_jpg.rf.6f2df7277e8b14a7d72f7b73bb9a83e5.jpg  \n",
            " extracting: valid/batch-6-075600_jpg.rf.44c8978b4af671f52256fb5e7f795056.jpg  \n",
            " extracting: valid/batch-6-075630_jpg.rf.3bb56fbc6efe554e28291a01390a80c4.jpg  \n",
            " extracting: valid/batch-6-075780_jpg.rf.a86712d0e22624389c43ab438451c093.jpg  \n",
            " extracting: valid/batch-6-075840_jpg.rf.77e70068cb3e5eb81aa51bc9c53b8557.jpg  \n",
            " extracting: valid/batch-6-075900_jpg.rf.e9469f257ddd276eaa1c4ef67992bad4.jpg  \n",
            " extracting: valid/batch-6-076080_jpg.rf.be02ac8947070f9f56260329f82e5c72.jpg  \n",
            " extracting: valid/batch-6-076110_jpg.rf.e0f5d3f16fc8744cda41479381679d1d.jpg  \n",
            " extracting: valid/batch-6-076860_jpg.rf.9f7b92b540ec4c7ebb639ab391591aa6.jpg  \n",
            " extracting: valid/batch-6-076920_jpg.rf.cdb7f14731d80e3a3eb497776116b3af.jpg  \n",
            " extracting: valid/batch-6-076950_jpg.rf.e5d102198ab75b672dc1d2e436f5b527.jpg  \n",
            " extracting: valid/batch-6-077160_jpg.rf.52b4aa030598f16d60fb3491dae45431.jpg  \n",
            " extracting: valid/batch-6-077310_jpg.rf.978a614f93ae7cde396b422e27b4ae72.jpg  \n",
            " extracting: valid/batch-6-077400_jpg.rf.0348446512ba82cf4b6cd29d2c7846d9.jpg  \n",
            " extracting: valid/batch-6-077430_jpg.rf.72b41e9537a58404440d354ed57cd592.jpg  \n",
            " extracting: valid/batch-6-077610_jpg.rf.6c08cbf575794fae1863df8f0dba848a.jpg  \n",
            " extracting: valid/batch-6-077670_jpg.rf.188f98f495b851d9b5385e67d0d78232.jpg  \n",
            " extracting: valid/batch-6-077760_jpg.rf.6723ff8bf1ac26b7d7d8d2e3c357b7d7.jpg  \n",
            " extracting: valid/batch-6-078030_jpg.rf.1d31925e20d5a418cb01a7fa512f7a57.jpg  \n",
            " extracting: valid/batch-6-078180_jpg.rf.1dfd47c0c0c473815cc42e35aef82fb7.jpg  \n",
            " extracting: valid/batch-6-078210_jpg.rf.ec635b985158427eb0a261d6266a4655.jpg  \n",
            " extracting: valid/batch-6-078420_jpg.rf.c8caf4d9a82004c794405826cb245dba.jpg  \n",
            " extracting: valid/batch-6-078540_jpg.rf.720819e909cfaba8d0c8b3fe42d42200.jpg  \n",
            " extracting: valid/batch-6-078810_jpg.rf.daa7c7d88d324d891cb97009523877e5.jpg  \n",
            " extracting: valid/batch-6-079830_jpg.rf.d207d5bac642296ff4a61f652aba531c.jpg  \n",
            " extracting: valid/batch-6-080460_jpg.rf.618210ebc2d6bc947d9fb61efe6664e2.jpg  \n",
            " extracting: valid/batch-6-080580_jpg.rf.6cdf3c5fcd29cb0c645325bdf24443a0.jpg  \n",
            " extracting: valid/batch-6-081540_jpg.rf.817ffff1456da11cf75997af6ebcbe03.jpg  \n",
            " extracting: valid/batch-6-081720_jpg.rf.865cb1b2035cbca8fed1d251826cc88e.jpg  \n",
            " extracting: valid/batch-6-082020_jpg.rf.c5703a5b5318c3b2b7d51fa1ac31c18b.jpg  \n",
            " extracting: valid/batch-6-082050_jpg.rf.0fae841f39fcca3a88de10ab0c3a5940.jpg  \n",
            " extracting: valid/batch-6-082080_jpg.rf.444dac542c7694146832b8267d454cec.jpg  \n",
            " extracting: valid/batch-6-082170_jpg.rf.1b9b9cb7c2d91c62efe86e240afd4e20.jpg  \n",
            " extracting: valid/batch-6-082320_jpg.rf.6fcc0feff45e3a32c005aa7f018ada65.jpg  \n",
            " extracting: valid/batch-6-082590_jpg.rf.140a15ab78b3c3681e85b05531197b2d.jpg  \n",
            " extracting: valid/batch-6-082710_jpg.rf.08a86f2748eacc7150be78d6f9335efe.jpg  \n",
            " extracting: valid/batch-6-082770_jpg.rf.1226fea301f742fe78bff99734261e71.jpg  \n",
            " extracting: valid/batch-6-082890_jpg.rf.de7cde10c816b32dd9fafa62d376f6f5.jpg  \n",
            " extracting: valid/batch-6-084420_jpg.rf.2e205b12261c7c887d3df8e7f4904c2b.jpg  \n",
            " extracting: valid/batch-6-084540_jpg.rf.23bd721405b55f65331168784a75a92c.jpg  \n",
            " extracting: valid/batch-6-084570_jpg.rf.8f12264639427242cfacddf098b0924d.jpg  \n",
            " extracting: valid/batch-6-084600_jpg.rf.7615b7dc41883acd47fe10e270fe4c15.jpg  \n",
            " extracting: valid/batch-6-084630_jpg.rf.5436909ce5e589603d3456042cecebce.jpg  \n",
            " extracting: valid/batch-6-085620_jpg.rf.da80461379ffa7229b1a616a32721fa8.jpg  \n",
            " extracting: valid/batch-6-085770_jpg.rf.5db36c498b8be67f869c4d28a9b0e90a.jpg  \n",
            " extracting: valid/batch-6-087630_jpg.rf.70b92299ae749ff42a86eedbb0b9705d.jpg  \n",
            " extracting: valid/batch-6-088020_jpg.rf.fcf2813ee49f3b2147d20db943863e42.jpg  \n",
            " extracting: valid/batch-6-088170_jpg.rf.99a9f4ef4e3b063b13778fe75234bda5.jpg  \n",
            " extracting: valid/batch-6-088200_jpg.rf.6b92db49be6f9e9b5bbaef13fc642b0c.jpg  \n",
            " extracting: valid/batch-6-088530_jpg.rf.8e7831623750b8951387e68285271856.jpg  \n",
            " extracting: valid/batch-6-088890_jpg.rf.6354aaeb8b692f933e4349914777e355.jpg  \n",
            " extracting: valid/batch-6-088920_jpg.rf.5249435ec588736740ed9c251f417a30.jpg  \n",
            " extracting: valid/batch-6-088980_jpg.rf.765f56ae297787574e97143a544cd278.jpg  \n",
            " extracting: valid/batch-6-089040_jpg.rf.faebd8db0bb8a71ae4c68bce61681e44.jpg  \n",
            " extracting: valid/batch-6-089220_jpg.rf.8d36eb6178a17c94fcf9e53debf65c04.jpg  \n",
            " extracting: valid/batch-6-089460_jpg.rf.04588cfd87dbdf2acfa53a7e35670b68.jpg  \n",
            " extracting: valid/batch-6-089520_jpg.rf.cd837e0bc9f0918cbf68129979888049.jpg  \n",
            " extracting: valid/batch-6-090000_jpg.rf.a500db00bbcd54a74bb64894a4516d43.jpg  \n",
            " extracting: valid/batch-6-090120_jpg.rf.40d304e657c3af452f7550c982d1b3a5.jpg  \n",
            " extracting: valid/batch-6-090180_jpg.rf.e67396350cc8fbaab9578342b1287164.jpg  \n",
            " extracting: valid/batch-6-090240_jpg.rf.e3c5819001230d39ba7c2e46c4f9719f.jpg  \n",
            " extracting: valid/batch-6-090300_jpg.rf.75d5706e85ee7e8cad8df072d4404479.jpg  \n",
            " extracting: valid/batch-6-090330_jpg.rf.7c82292cd93aba12f416d3cde6fdac0c.jpg  \n",
            " extracting: valid/batch-6-090390_jpg.rf.db3f522eeb72446637be839c57898ab0.jpg  \n",
            " extracting: valid/batch-6-090630_jpg.rf.eed41096ffab63e4952e2467510e753d.jpg  \n",
            " extracting: valid/batch-6-090690_jpg.rf.d23aa254bd45fb80159f8c48959e22c9.jpg  \n",
            " extracting: valid/batch-6-092910_jpg.rf.f3e0a7469d4e5fc7d6401ff97c261d4d.jpg  \n",
            " extracting: valid/batch-6-093300_jpg.rf.f2c40a934ba3af30057121ca56db6e83.jpg  \n",
            " extracting: valid/batch-6-093360_jpg.rf.27384ae509faf8039305ab88f12fbe5a.jpg  \n",
            " extracting: valid/batch-6-093390_jpg.rf.28cf622375eefa8f9610f1af5432b7c8.jpg  \n",
            " extracting: valid/batch-6-093420_jpg.rf.eb6a2492af0b6cbb12776498c65ae7fa.jpg  \n",
            " extracting: valid/batch-6-093840_jpg.rf.ff0899d2cf0473697a766d3a4b4483fa.jpg  \n",
            " extracting: valid/batch-6-094350_jpg.rf.fa3b42ae8be08f9092a57ee1b442d1b3.jpg  \n",
            " extracting: valid/batch-6-094380_jpg.rf.11789197480cb988721f131f249d65ee.jpg  \n",
            " extracting: valid/batch-6-095070_jpg.rf.d86cb132863ccbe857b5bceecdcc1d1e.jpg  \n",
            " extracting: valid/batch-6-095160_jpg.rf.e19879485357eadb8de9758088669837.jpg  \n",
            " extracting: valid/batch-6-101910_jpg.rf.e99968bce92095efae103ea802742f3c.jpg  \n",
            " extracting: valid/batch-6-102690_jpg.rf.eba1e345fdaced1810426fd68a54ad5c.jpg  \n",
            " extracting: valid/batch-6-103620_jpg.rf.35133ef484ab67fb0869dd3a5fbca953.jpg  \n",
            " extracting: valid/batch-6-103710_jpg.rf.dbcc31c55628a6cb31646e72a22128cf.jpg  \n",
            " extracting: valid/batch-6-104010_jpg.rf.5afc47b5c2b4a8c54f0fe7cf1a322dd3.jpg  \n",
            " extracting: valid/batch-6-104040_jpg.rf.3d6e1b161ab006b26c13c1f1b283a4be.jpg  \n",
            " extracting: valid/batch-6-104070_jpg.rf.9e7b5e2bfcf1cd159f79c9814edcebc8.jpg  \n",
            " extracting: valid/batch-6-104130_jpg.rf.6d1fff0a028f2ffd1d2b8737815a4d57.jpg  \n",
            " extracting: valid/batch-6-104160_jpg.rf.4ad12aa0815c7981838b0195374949ab.jpg  \n",
            " extracting: valid/batch-6-104220_jpg.rf.a48ab502416172b0b7905eabbc52a024.jpg  \n",
            " extracting: valid/batch-6-104520_jpg.rf.ce0b5dfdbc522c3e85f83b46d19396ef.jpg  \n",
            " extracting: valid/batch-6-104790_jpg.rf.a47c007f5b872b0fbc7d62cb8ec9a1db.jpg  \n",
            " extracting: valid/batch-6-104940_jpg.rf.da5666367f37d64cdeed3f2dc0d3f285.jpg  \n",
            " extracting: valid/batch-6-105060_jpg.rf.03b3290a4c2e6ff443e2b56a64e0c7c0.jpg  \n",
            " extracting: valid/batch-6-105180_jpg.rf.83b97fb159ffd33d76961201e5fb257b.jpg  \n",
            " extracting: valid/batch-6-105510_jpg.rf.184e46433c9662de996685f640e46ae2.jpg  \n",
            " extracting: valid/batch-6-105600_jpg.rf.6439d4a49a9a407ad0200f666170115f.jpg  \n",
            " extracting: valid/batch-6-105750_jpg.rf.c5f2eb7c186c1ee6bd216aca404317b9.jpg  \n",
            " extracting: valid/batch-6-105930_jpg.rf.291cd75eda7913a9da9136352ab01227.jpg  \n",
            " extracting: valid/batch-6-106020_jpg.rf.9b57d8ab9c8a5dc727ed4da1b25bbb4e.jpg  \n",
            " extracting: valid/batch-6-106110_jpg.rf.ac14790d060b1a748d3b256d669c919e.jpg  \n",
            " extracting: valid/batch-6-106230_jpg.rf.596806f61569271d6e770b36e1d57e01.jpg  \n",
            " extracting: valid/batch-6-106380_jpg.rf.84ed5089206e1190f0274cdec22c9d07.jpg  \n",
            " extracting: valid/batch-6-106590_jpg.rf.4c427bdf6d98f902433ba2b2037fa034.jpg  \n",
            " extracting: valid/batch-6-106830_jpg.rf.e799917f28f10be4cbd43863c0a21ac0.jpg  \n",
            " extracting: valid/batch-6-106860_jpg.rf.299f58df5d431e4da0899b428fede3b0.jpg  \n",
            " extracting: valid/batch-6-107190_jpg.rf.99ec4ec6bdd001de98040466be4d1c69.jpg  \n",
            " extracting: valid/batch-6-107550_jpg.rf.caa9d9fe526f867db0a8dcff0c84c207.jpg  \n",
            " extracting: valid/batch-6-107700_jpg.rf.a522beac196741bbf8bcb687b591d4dd.jpg  \n",
            " extracting: valid/batch-6-107730_jpg.rf.ea9a3ff4de2b1a1be3ce50dd647ca8a7.jpg  \n",
            " extracting: valid/batch-6-107760_jpg.rf.21badd58fce28dd7860551a9ca81216f.jpg  \n",
            " extracting: valid/batch-6-107820_jpg.rf.27adc0c90bb0aae8a6f5a9d854191c4b.jpg  \n",
            " extracting: valid/batch-6-107880_jpg.rf.ace04c84f64034ac5318e404390ac73e.jpg  \n",
            " extracting: valid/batch-6-108210_jpg.rf.3f9a20657e37b170dd49a174ba08d56f.jpg  \n",
            " extracting: valid/batch-6-108240_jpg.rf.448b188ef71b2ebfb7ee8adda538a188.jpg  \n",
            " extracting: valid/batch-6-108270_jpg.rf.f6f76d6a3b99ed4a3d3f859e2e77f82d.jpg  \n",
            " extracting: valid/batch-6-108390_jpg.rf.a12382be8fde7efbd323c97d411f073e.jpg  \n",
            " extracting: valid/batch-6-108600_jpg.rf.24a251ab99c2121f7b36d256b95d3d18.jpg  \n",
            " extracting: valid/batch-6-109590_jpg.rf.0d008a0784070615a4e055a4cb0fc04d.jpg  \n",
            " extracting: valid/batch-6-109800_jpg.rf.aacf252431ffa9ed9fabe3dbcb02fb7e.jpg  \n",
            " extracting: valid/batch-6-109980_jpg.rf.7074c5900834e0b7401ce6b0e5bcf023.jpg  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAoLxBEEz4FW"
      },
      "source": [
        "%cp train/_annotations.txt train/train.txt\n",
        "%cp train/_annotations.txt train.txt\n",
        "%cp valid/_annotations.txt data/val.txt\n",
        "%cp valid/*.jpg train/"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvwlqXkSlwUm"
      },
      "source": [
        "def file_len(fname):\n",
        "  with open(fname) as f:\n",
        "    for i, l in enumerate(f):\n",
        "      pass\n",
        "  return i + 1\n",
        "\n",
        "num_classes = file_len('train/_classes.txt')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6xrTj1gREEc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b714fe35-b3b0-489c-f425-48e2777badcb"
      },
      "source": [
        "print(num_classes)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffDU8jImv8_S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55c99aac-88e6-427b-f53d-8ceb5e253539"
      },
      "source": [
        "%%writefile /content/pytorch-YOLOv4/dataset.py\n",
        "# -*- coding: utf-8 -*-\n",
        "'''\n",
        "@Time          : 2020/05/06 21:09\n",
        "@Author        : Tianxiaomo\n",
        "@File          : dataset.py\n",
        "@Noice         :\n",
        "@Modificattion :\n",
        "    @Author    :\n",
        "    @Time      :\n",
        "    @Detail    :\n",
        "\n",
        "'''\n",
        "from torch.utils.data.dataset import Dataset\n",
        "\n",
        "import random\n",
        "import cv2\n",
        "import sys\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def rand_uniform_strong(min, max):\n",
        "    if min > max:\n",
        "        swap = min\n",
        "        min = max\n",
        "        max = swap\n",
        "    return random.random() * (max - min) + min\n",
        "\n",
        "\n",
        "def rand_scale(s):\n",
        "    scale = rand_uniform_strong(1, s)\n",
        "    if random.randint(0, 1) % 2:\n",
        "        return scale\n",
        "    return 1. / scale\n",
        "\n",
        "\n",
        "def rand_precalc_random(min, max, random_part):\n",
        "    if max < min:\n",
        "        swap = min\n",
        "        min = max\n",
        "        max = swap\n",
        "    return (random_part * (max - min)) + min\n",
        "\n",
        "\n",
        "def fill_truth_detection(bboxes, num_boxes, classes, flip, dx, dy, sx, sy, net_w, net_h):\n",
        "    if bboxes.shape[0] == 0:\n",
        "        return bboxes, 10000\n",
        "    np.random.shuffle(bboxes)\n",
        "    bboxes[:, 0] -= dx\n",
        "    bboxes[:, 2] -= dx\n",
        "    bboxes[:, 1] -= dy\n",
        "    bboxes[:, 3] -= dy\n",
        "\n",
        "    bboxes[:, 0] = np.clip(bboxes[:, 0], 0, sx)\n",
        "    bboxes[:, 2] = np.clip(bboxes[:, 2], 0, sx)\n",
        "\n",
        "    bboxes[:, 1] = np.clip(bboxes[:, 1], 0, sy)\n",
        "    bboxes[:, 3] = np.clip(bboxes[:, 3], 0, sy)\n",
        "\n",
        "    out_box = list(np.where(((bboxes[:, 1] == sy) & (bboxes[:, 3] == sy)) |\n",
        "                            ((bboxes[:, 0] == sx) & (bboxes[:, 2] == sx)) |\n",
        "                            ((bboxes[:, 1] == 0) & (bboxes[:, 3] == 0)) |\n",
        "                            ((bboxes[:, 0] == 0) & (bboxes[:, 2] == 0)))[0])\n",
        "    list_box = list(range(bboxes.shape[0]))\n",
        "    for i in out_box:\n",
        "        list_box.remove(i)\n",
        "    bboxes = bboxes[list_box]\n",
        "\n",
        "    if bboxes.shape[0] == 0:\n",
        "        return bboxes, 10000\n",
        "\n",
        "    bboxes = bboxes[np.where((bboxes[:, 4] < classes) & (bboxes[:, 4] >= 0))[0]]\n",
        "\n",
        "    if bboxes.shape[0] > num_boxes:\n",
        "        bboxes = bboxes[:num_boxes]\n",
        "\n",
        "    min_w_h = np.array([bboxes[:, 2] - bboxes[:, 0], bboxes[:, 3] - bboxes[:, 1]]).min()\n",
        "\n",
        "    bboxes[:, 0] *= (net_w / sx)\n",
        "    bboxes[:, 2] *= (net_w / sx)\n",
        "    bboxes[:, 1] *= (net_h / sy)\n",
        "    bboxes[:, 3] *= (net_h / sy)\n",
        "\n",
        "    if flip:\n",
        "        temp = net_w - bboxes[:, 0]\n",
        "        bboxes[:, 0] = net_w - bboxes[:, 2]\n",
        "        bboxes[:, 2] = temp\n",
        "\n",
        "    return bboxes, min_w_h\n",
        "\n",
        "\n",
        "def rect_intersection(a, b):\n",
        "    minx = max(a[0], b[0])\n",
        "    miny = max(a[1], b[1])\n",
        "\n",
        "    maxx = min(a[2], b[2])\n",
        "    maxy = min(a[3], b[3])\n",
        "    return [minx, miny, maxx, maxy]\n",
        "\n",
        "\n",
        "def image_data_augmentation(mat, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, gaussian_noise, blur,\n",
        "                            truth):\n",
        "    try:\n",
        "        img = mat\n",
        "        oh, ow, _ = img.shape\n",
        "        pleft, ptop, swidth, sheight = int(pleft), int(ptop), int(swidth), int(sheight)\n",
        "        # crop\n",
        "        src_rect = [pleft, ptop, swidth + pleft, sheight + ptop]  # x1,y1,x2,y2\n",
        "        img_rect = [0, 0, ow, oh]\n",
        "        new_src_rect = rect_intersection(src_rect, img_rect)  # 交集\n",
        "\n",
        "        dst_rect = [max(0, -pleft), max(0, -ptop), max(0, -pleft) + new_src_rect[2] - new_src_rect[0],\n",
        "                    max(0, -ptop) + new_src_rect[3] - new_src_rect[1]]\n",
        "        # cv2.Mat sized\n",
        "\n",
        "        if (src_rect[0] == 0 and src_rect[1] == 0 and src_rect[2] == img.shape[0] and src_rect[3] == img.shape[1]):\n",
        "            sized = cv2.resize(img, (w, h), cv2.INTER_LINEAR)\n",
        "        else:\n",
        "            cropped = np.zeros([sheight, swidth, 3])\n",
        "            cropped[:, :, ] = np.mean(img, axis=(0, 1))\n",
        "\n",
        "            cropped[dst_rect[1]:dst_rect[3], dst_rect[0]:dst_rect[2]] = \\\n",
        "                img[new_src_rect[1]:new_src_rect[3], new_src_rect[0]:new_src_rect[2]]\n",
        "\n",
        "            # resize\n",
        "            sized = cv2.resize(cropped, (w, h), cv2.INTER_LINEAR)\n",
        "\n",
        "        # flip\n",
        "        if flip:\n",
        "            # cv2.Mat cropped\n",
        "            sized = cv2.flip(sized, 1)  # 0 - x-axis, 1 - y-axis, -1 - both axes (x & y)\n",
        "\n",
        "        # HSV augmentation\n",
        "        # cv2.COLOR_BGR2HSV, cv2.COLOR_RGB2HSV, cv2.COLOR_HSV2BGR, cv2.COLOR_HSV2RGB\n",
        "        if dsat != 1 or dexp != 1 or dhue != 0:\n",
        "            if img.shape[2] >= 3:\n",
        "                hsv_src = cv2.cvtColor(sized.astype(np.float32), cv2.COLOR_RGB2HSV)  # RGB to HSV\n",
        "                hsv = cv2.split(hsv_src)\n",
        "                hsv[1] *= dsat\n",
        "                hsv[2] *= dexp\n",
        "                hsv[0] += 179 * dhue\n",
        "                hsv_src = cv2.merge(hsv)\n",
        "                sized = np.clip(cv2.cvtColor(hsv_src, cv2.COLOR_HSV2RGB), 0, 255)  # HSV to RGB (the same as previous)\n",
        "            else:\n",
        "                sized *= dexp\n",
        "\n",
        "        if blur:\n",
        "            if blur == 1:\n",
        "                dst = cv2.GaussianBlur(sized, (17, 17), 0)\n",
        "                # cv2.bilateralFilter(sized, dst, 17, 75, 75)\n",
        "            else:\n",
        "                ksize = (blur / 2) * 2 + 1\n",
        "                dst = cv2.GaussianBlur(sized, (ksize, ksize), 0)\n",
        "\n",
        "            if blur == 1:\n",
        "                img_rect = [0, 0, sized.cols, sized.rows]\n",
        "                for b in truth:\n",
        "                    left = (b.x - b.w / 2.) * sized.shape[1]\n",
        "                    width = b.w * sized.shape[1]\n",
        "                    top = (b.y - b.h / 2.) * sized.shape[0]\n",
        "                    height = b.h * sized.shape[0]\n",
        "                    roi(left, top, width, height)\n",
        "                    roi = roi & img_rect\n",
        "                    dst[roi[0]:roi[0] + roi[2], roi[1]:roi[1] + roi[3]] = sized[roi[0]:roi[0] + roi[2],\n",
        "                                                                          roi[1]:roi[1] + roi[3]]\n",
        "\n",
        "            sized = dst\n",
        "\n",
        "        if gaussian_noise:\n",
        "            noise = np.array(sized.shape)\n",
        "            gaussian_noise = min(gaussian_noise, 127)\n",
        "            gaussian_noise = max(gaussian_noise, 0)\n",
        "            cv2.randn(noise, 0, gaussian_noise)  # mean and variance\n",
        "            sized = sized + noise\n",
        "    except:\n",
        "        print(\"OpenCV can't augment image: \" + str(w) + \" x \" + str(h))\n",
        "        sized = mat\n",
        "\n",
        "    return sized\n",
        "\n",
        "\n",
        "def filter_truth(bboxes, dx, dy, sx, sy, xd, yd):\n",
        "    bboxes[:, 0] -= dx\n",
        "    bboxes[:, 2] -= dx\n",
        "    bboxes[:, 1] -= dy\n",
        "    bboxes[:, 3] -= dy\n",
        "\n",
        "    bboxes[:, 0] = np.clip(bboxes[:, 0], 0, sx)\n",
        "    bboxes[:, 2] = np.clip(bboxes[:, 2], 0, sx)\n",
        "\n",
        "    bboxes[:, 1] = np.clip(bboxes[:, 1], 0, sy)\n",
        "    bboxes[:, 3] = np.clip(bboxes[:, 3], 0, sy)\n",
        "\n",
        "    out_box = list(np.where(((bboxes[:, 1] == sy) & (bboxes[:, 3] == sy)) |\n",
        "                            ((bboxes[:, 0] == sx) & (bboxes[:, 2] == sx)) |\n",
        "                            ((bboxes[:, 1] == 0) & (bboxes[:, 3] == 0)) |\n",
        "                            ((bboxes[:, 0] == 0) & (bboxes[:, 2] == 0)))[0])\n",
        "    list_box = list(range(bboxes.shape[0]))\n",
        "    for i in out_box:\n",
        "        list_box.remove(i)\n",
        "    bboxes = bboxes[list_box]\n",
        "\n",
        "    bboxes[:, 0] += xd\n",
        "    bboxes[:, 2] += xd\n",
        "    bboxes[:, 1] += yd\n",
        "    bboxes[:, 3] += yd\n",
        "\n",
        "    return bboxes\n",
        "\n",
        "\n",
        "def blend_truth_mosaic(out_img, img, bboxes, w, h, cut_x, cut_y, i_mixup,\n",
        "                       left_shift, right_shift, top_shift, bot_shift):\n",
        "    left_shift = min(left_shift, w - cut_x)\n",
        "    top_shift = min(top_shift, h - cut_y)\n",
        "    right_shift = min(right_shift, cut_x)\n",
        "    bot_shift = min(bot_shift, cut_y)\n",
        "\n",
        "    if i_mixup == 0:\n",
        "        bboxes = filter_truth(bboxes, left_shift, top_shift, cut_x, cut_y, 0, 0)\n",
        "        out_img[:cut_y, :cut_x] = img[top_shift:top_shift + cut_y, left_shift:left_shift + cut_x]\n",
        "    if i_mixup == 1:\n",
        "        bboxes = filter_truth(bboxes, cut_x - right_shift, top_shift, w - cut_x, cut_y, cut_x, 0)\n",
        "        out_img[:cut_y, cut_x:] = img[top_shift:top_shift + cut_y, cut_x - right_shift:w - right_shift]\n",
        "    if i_mixup == 2:\n",
        "        bboxes = filter_truth(bboxes, left_shift, cut_y - bot_shift, cut_x, h - cut_y, 0, cut_y)\n",
        "        out_img[cut_y:, :cut_x] = img[cut_y - bot_shift:h - bot_shift, left_shift:left_shift + cut_x]\n",
        "    if i_mixup == 3:\n",
        "        bboxes = filter_truth(bboxes, cut_x - right_shift, cut_y - bot_shift, w - cut_x, h - cut_y, cut_x, cut_y)\n",
        "        out_img[cut_y:, cut_x:] = img[cut_y - bot_shift:h - bot_shift, cut_x - right_shift:w - right_shift]\n",
        "\n",
        "    return out_img, bboxes\n",
        "\n",
        "\n",
        "def draw_box(img, bboxes):\n",
        "    for b in bboxes:\n",
        "        img = cv2.rectangle(img, (b[0], b[1]), (b[2], b[3]), (0, 255, 0), 2)\n",
        "    return img\n",
        "\n",
        "\n",
        "class Yolo_dataset(Dataset):\n",
        "    def __init__(self, lable_path, cfg):\n",
        "        super(Yolo_dataset, self).__init__()\n",
        "        if cfg.mixup == 2:\n",
        "            print(\"cutmix=1 - isn't supported for Detector\")\n",
        "            raise\n",
        "        elif cfg.mixup == 2 and cfg.letter_box:\n",
        "            print(\"Combination: letter_box=1 & mosaic=1 - isn't supported, use only 1 of these parameters\")\n",
        "            raise\n",
        "\n",
        "        self.cfg = cfg\n",
        "\n",
        "        truth = {}\n",
        "        f = open(lable_path, 'r', encoding='utf-8')\n",
        "        for line in f.readlines():\n",
        "            data = line.split(\" \")\n",
        "            truth[data[0]] = []\n",
        "            for i in data[1:]:\n",
        "                truth[data[0]].append([int(j) for j in i.split(',')])\n",
        "\n",
        "        self.truth = truth\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.truth.keys())\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        img_path = list(self.truth.keys())[index]\n",
        "        bboxes = np.array(self.truth.get(img_path), dtype=np.float)\n",
        "        img_path = os.path.join(self.cfg.dataset_dir, img_path)\n",
        "        use_mixup = self.cfg.mixup\n",
        "        if random.randint(0, 1):\n",
        "            use_mixup = 0\n",
        "\n",
        "        if use_mixup == 3:\n",
        "            min_offset = 0.2\n",
        "            cut_x = random.randint(int(self.cfg.w * min_offset), int(self.cfg.w * (1 - min_offset)))\n",
        "            cut_y = random.randint(int(self.cfg.h * min_offset), int(self.cfg.h * (1 - min_offset)))\n",
        "\n",
        "        r1, r2, r3, r4, r_scale = 0, 0, 0, 0, 0\n",
        "        dhue, dsat, dexp, flip, blur = 0, 0, 0, 0, 0\n",
        "        gaussian_noise = 0\n",
        "\n",
        "        out_img = np.zeros([self.cfg.h, self.cfg.w, 3])\n",
        "        out_bboxes = []\n",
        "\n",
        "        for i in range(use_mixup + 1):\n",
        "            if i != 0:\n",
        "                img_path = random.choice(list(self.truth.keys()))\n",
        "                bboxes = np.array(self.truth.get(img_path), dtype=np.float)\n",
        "                img_path = os.path.join(self.cfg.dataset_dir, img_path)\n",
        "            #print(img_path)\n",
        "            img = cv2.imread(img_path)\n",
        "            #print(img)\n",
        "            if img is None:\n",
        "                continue\n",
        "            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "            oh, ow, oc = img.shape\n",
        "            dh, dw, dc = np.array(np.array([oh, ow, oc]) * self.cfg.jitter, dtype=np.int)\n",
        "\n",
        "            dhue = rand_uniform_strong(-self.cfg.hue, self.cfg.hue)\n",
        "            dsat = rand_scale(self.cfg.saturation)\n",
        "            dexp = rand_scale(self.cfg.exposure)\n",
        "\n",
        "            pleft = random.randint(-dw, dw)\n",
        "            pright = random.randint(-dw, dw)\n",
        "            ptop = random.randint(-dh, dh)\n",
        "            pbot = random.randint(-dh, dh)\n",
        "\n",
        "            flip = random.randint(0, 1) if self.cfg.flip else 0\n",
        "\n",
        "            if (self.cfg.blur):\n",
        "                tmp_blur = random.randint(0, 2)  # 0 - disable, 1 - blur background, 2 - blur the whole image\n",
        "                if tmp_blur == 0:\n",
        "                    blur = 0\n",
        "                elif tmp_blur == 1:\n",
        "                    blur = 1\n",
        "                else:\n",
        "                    blur = self.cfg.blur\n",
        "\n",
        "            if self.cfg.gaussian and random.randint(0, 1):\n",
        "                gaussian_noise = self.cfg.gaussian\n",
        "            else:\n",
        "                gaussian_noise = 0\n",
        "\n",
        "            if self.cfg.letter_box:\n",
        "                img_ar = ow / oh\n",
        "                net_ar = self.cfg.w / self.cfg.h\n",
        "                result_ar = img_ar / net_ar\n",
        "                # print(\" ow = %d, oh = %d, w = %d, h = %d, img_ar = %f, net_ar = %f, result_ar = %f \\n\", ow, oh, w, h, img_ar, net_ar, result_ar);\n",
        "                if result_ar > 1:  # sheight - should be increased\n",
        "                    oh_tmp = ow / net_ar\n",
        "                    delta_h = (oh_tmp - oh) / 2\n",
        "                    ptop = ptop - delta_h\n",
        "                    pbot = pbot - delta_h\n",
        "                    # print(\" result_ar = %f, oh_tmp = %f, delta_h = %d, ptop = %f, pbot = %f \\n\", result_ar, oh_tmp, delta_h, ptop, pbot);\n",
        "                else:  # swidth - should be increased\n",
        "                    ow_tmp = oh * net_ar\n",
        "                    delta_w = (ow_tmp - ow) / 2\n",
        "                    pleft = pleft - delta_w\n",
        "                    pright = pright - delta_w\n",
        "                    # printf(\" result_ar = %f, ow_tmp = %f, delta_w = %d, pleft = %f, pright = %f \\n\", result_ar, ow_tmp, delta_w, pleft, pright);\n",
        "\n",
        "            swidth = ow - pleft - pright\n",
        "            sheight = oh - ptop - pbot\n",
        "\n",
        "            truth, min_w_h = fill_truth_detection(bboxes, self.cfg.boxes, self.cfg.classes, flip, pleft, ptop, swidth,\n",
        "                                                  sheight, self.cfg.w, self.cfg.h)\n",
        "            if (min_w_h / 8) < blur and blur > 1:  # disable blur if one of the objects is too small\n",
        "                blur = min_w_h / 8\n",
        "\n",
        "            ai = image_data_augmentation(img, self.cfg.w, self.cfg.h, pleft, ptop, swidth, sheight, flip,\n",
        "                                         dhue, dsat, dexp, gaussian_noise, blur, truth)\n",
        "\n",
        "            if use_mixup == 0:\n",
        "                out_img = ai\n",
        "                out_bboxes = truth\n",
        "            if use_mixup == 1:\n",
        "                if i == 0:\n",
        "                    old_img = ai.copy()\n",
        "                    old_truth = truth.copy()\n",
        "                elif i == 1:\n",
        "                    out_img = cv2.addWeighted(ai, 0.5, old_img, 0.5)\n",
        "                    out_bboxes = np.concatenate([old_truth, truth], axis=0)\n",
        "            elif use_mixup == 3:\n",
        "                if flip:\n",
        "                    tmp = pleft\n",
        "                    pleft = pright\n",
        "                    pright = tmp\n",
        "\n",
        "                left_shift = int(min(cut_x, max(0, (-int(pleft) * self.cfg.w / swidth))))\n",
        "                top_shift = int(min(cut_y, max(0, (-int(ptop) * self.cfg.h / sheight))))\n",
        "\n",
        "                right_shift = int(min((self.cfg.w - cut_x), max(0, (-int(pright) * self.cfg.w / swidth))))\n",
        "                bot_shift = int(min(self.cfg.h - cut_y, max(0, (-int(pbot) * self.cfg.h / sheight))))\n",
        "\n",
        "                out_img, out_bbox = blend_truth_mosaic(out_img, ai, truth.copy(), self.cfg.w, self.cfg.h, cut_x,\n",
        "                                                       cut_y, i, left_shift, right_shift, top_shift, bot_shift)\n",
        "                out_bboxes.append(out_bbox)\n",
        "                # print(img_path)\n",
        "        if use_mixup == 3:\n",
        "            out_bboxes = np.concatenate(out_bboxes, axis=0)\n",
        "        out_bboxes1 = np.zeros([self.cfg.boxes, 5])\n",
        "        out_bboxes1[:min(out_bboxes.shape[0], self.cfg.boxes)] = out_bboxes[:min(out_bboxes.shape[0], self.cfg.boxes)]\n",
        "        return out_img, out_bboxes1\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    from cfg import Cfg\n",
        "\n",
        "    random.seed(2020)\n",
        "    np.random.seed(2020)\n",
        "    Cfg.dataset_dir = '/mnt/e/Dataset'\n",
        "    dataset = Yolo_dataset(Cfg.train_label, Cfg)\n",
        "    for i in range(100):\n",
        "        out_img, out_bboxes = dataset.__getitem__(i)\n",
        "        a = draw_box(out_img.copy(), out_bboxes.astype(np.int32))\n",
        "        plt.imshow(a.astype(np.int32))\n",
        "        plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting /content/pytorch-YOLOv4/dataset.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uly0ZrqGjR9w"
      },
      "source": [
        "# Train Custom Detector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIlhqP2S57Ub",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d918d1da-bc0d-4c30-b691-1d02f2750551"
      },
      "source": [
        "#start training\n",
        "#-b batch size (you should keep this low (2-4) for training to work properly)\n",
        "#-s number of subdivisions in the batch, this was more relevant for the darknet framework\n",
        "#-l learning rate\n",
        "#-g direct training to the GPU device\n",
        "#pretrained invoke the pretrained weights that we downloaded above\n",
        "#classes - number of classes\n",
        "#dir - where the training data is\n",
        "#epoch - how long to train for\n",
        "!python train.py -b 2 -s 1 -l 0.001 -g 0 -pretrained ./yolov4.conv.137.pth -classes {num_classes} -dir ./train -epochs 50"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "log file path:log/log_2021-03-26_17-02-44.txt\n",
            "2021-03-26 17:02:44,657 train.py[line:435] INFO: Using device cuda\n",
            "2021-03-26 17:02:47,815 train.py[line:282] INFO: Starting training:\n",
            "        Epochs:          50\n",
            "        Batch size:      2\n",
            "        Subdivisions:    1\n",
            "        Learning rate:   0.001\n",
            "        Training size:   606\n",
            "        Validation size: 172\n",
            "        Checkpoints:     True\n",
            "        Device:          cuda\n",
            "        Images size:     608\n",
            "        Optimizer:       adam\n",
            "        Dataset classes: 5\n",
            "        Train label path:train.txt\n",
            "        Pretrained:\n",
            "    \n",
            "Epoch 1/50:   0%|        | 0/606 [00:00<?, ?img/s]/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
            "  warnings.warn(warning.format(ret))\n",
            "Epoch 1/50:   6%| | 38/606 [00:09<01:44,  5.45img//usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:224: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.\n",
            "  warnings.warn(\"To get the last learning rate computed by the scheduler, \"\n",
            "2021-03-26 17:02:57,570 train.py[line:353] INFO: Train step_20: loss : 32821.87890625,loss xy : 24.66356086730957,loss wh : 4.406064033508301,loss obj : 32760.630859375，loss cls : 32.18089294433594,loss l2 : 11956.779296875,lr : 1.6000000000000002e-10\n",
            "Epoch 1/50:  13%|▏| 78/606 [00:16<01:38,  5.38img/2021-03-26 17:03:05,062 train.py[line:353] INFO: Train step_40: loss : 32789.21484375,loss xy : 12.993325233459473,loss wh : 0.8711884021759033,loss obj : 32758.681640625，loss cls : 16.666093826293945,loss l2 : 11947.513671875,lr : 2.5600000000000003e-09\n",
            "Epoch 1/50:  19%|▏| 118/606 [00:24<01:31,  5.32img2021-03-26 17:03:12,559 train.py[line:353] INFO: Train step_60: loss : 32753.39453125,loss xy : 10.926587104797363,loss wh : 0.4201062321662903,loss obj : 32727.107421875，loss cls : 14.939455032348633,loss l2 : 11928.5107421875,lr : 1.2959999999999998e-08\n",
            "Epoch 1/50:  26%|▎| 158/606 [00:31<01:23,  5.36img2021-03-26 17:03:20,059 train.py[line:353] INFO: Train step_80: loss : 32763.916015625,loss xy : 12.009950637817383,loss wh : 1.1376456022262573,loss obj : 32735.3671875，loss cls : 15.401317596435547,loss l2 : 11939.54296875,lr : 4.0960000000000004e-08\n",
            "Epoch 1/50:  33%|▎| 198/606 [00:39<01:15,  5.38img2021-03-26 17:03:27,592 train.py[line:353] INFO: Train step_100: loss : 32766.81640625,loss xy : 27.8657169342041,loss wh : 5.535250186920166,loss obj : 32698.802734375，loss cls : 34.61358642578125,loss l2 : 11933.580078125,lr : 1.0000000000000002e-07\n",
            "Epoch 1/50:  39%|▍| 238/606 [00:46<01:09,  5.28img2021-03-26 17:03:35,179 train.py[line:353] INFO: Train step_120: loss : 32758.353515625,loss xy : 12.902530670166016,loss wh : 3.5366086959838867,loss obj : 32727.865234375，loss cls : 14.04842758178711,loss l2 : 11936.4755859375,lr : 2.0735999999999997e-07\n",
            "Epoch 1/50:  46%|▍| 278/606 [00:54<01:02,  5.21img2021-03-26 17:03:42,834 train.py[line:353] INFO: Train step_140: loss : 32698.578125,loss xy : 16.29339599609375,loss wh : 5.296629905700684,loss obj : 32651.564453125，loss cls : 25.42411231994629,loss l2 : 11911.6396484375,lr : 3.841600000000001e-07\n",
            "Epoch 1/50:  52%|▌| 318/606 [01:02<00:56,  5.13img2021-03-26 17:03:50,569 train.py[line:353] INFO: Train step_160: loss : 32648.29296875,loss xy : 7.924808979034424,loss wh : 2.6474766731262207,loss obj : 32626.970703125，loss cls : 10.750906944274902,loss l2 : 11885.3720703125,lr : 6.553600000000001e-07\n",
            "Epoch 1/50:  59%|▌| 358/606 [01:10<00:49,  5.02img2021-03-26 17:03:58,433 train.py[line:353] INFO: Train step_180: loss : 32579.32421875,loss xy : 20.541444778442383,loss wh : 5.17977237701416,loss obj : 32526.29296875，loss cls : 27.310754776000977,loss l2 : 11847.7666015625,lr : 1.0497599999999998e-06\n",
            "Epoch 1/50:  66%|▋| 398/606 [01:18<00:41,  5.06img2021-03-26 17:04:06,330 train.py[line:353] INFO: Train step_200: loss : 32461.658203125,loss xy : 8.216958045959473,loss wh : 0.8321162462234497,loss obj : 32442.11328125，loss cls : 10.496236801147461,loss l2 : 11795.8876953125,lr : 1.6000000000000004e-06\n",
            "Epoch 1/50:  72%|▋| 438/606 [01:26<00:33,  5.00img2021-03-26 17:04:14,280 train.py[line:353] INFO: Train step_220: loss : 32276.763671875,loss xy : 34.701507568359375,loss wh : 17.193002700805664,loss obj : 32181.689453125，loss cls : 43.17988204956055,loss l2 : 11714.705078125,lr : 2.34256e-06\n",
            "Epoch 1/50:  79%|▊| 478/606 [01:33<00:25,  5.03img2021-03-26 17:04:22,227 train.py[line:353] INFO: Train step_240: loss : 31864.05859375,loss xy : 16.1157283782959,loss wh : 7.531526565551758,loss obj : 31818.16015625，loss cls : 22.25140953063965,loss l2 : 11513.185546875,lr : 3.3177599999999996e-06\n",
            "Epoch 1/50:  85%|▊| 518/606 [01:42<00:17,  5.06img2021-03-26 17:04:30,243 train.py[line:353] INFO: Train step_260: loss : 31345.322265625,loss xy : 12.014063835144043,loss wh : 3.1630823612213135,loss obj : 31315.24609375，loss cls : 14.897794723510742,loss l2 : 11254.9853515625,lr : 4.569760000000001e-06\n",
            "Epoch 1/50:  92%|▉| 558/606 [01:49<00:09,  5.12img2021-03-26 17:04:38,137 train.py[line:353] INFO: Train step_280: loss : 30715.09765625,loss xy : 8.266605377197266,loss wh : 1.0350749492645264,loss obj : 30695.119140625，loss cls : 10.67771053314209,loss l2 : 10951.7568359375,lr : 6.146560000000002e-06\n",
            "Epoch 1/50:  99%|▉| 598/606 [01:57<00:01,  5.26img2021-03-26 17:04:45,868 train.py[line:353] INFO: Train step_300: loss : 29383.662109375,loss xy : 20.32958221435547,loss wh : 4.383522033691406,loss obj : 29333.490234375，loss cls : 25.459606170654297,loss l2 : 10295.146484375,lr : 8.1e-06\n",
            "Epoch 1/50: 100%|█| 606/606 [01:59<00:00,  5.26img2021-03-26 17:04:47,140 train.py[line:360] INFO: Created checkpoint directory\n",
            "2021-03-26 17:04:47,593 train.py[line:364] INFO: Checkpoint 1 saved !\n",
            "Epoch 1/50: 100%|█| 606/606 [01:59<00:00,  5.06img\n",
            "Epoch 2/50:   5%| | 32/606 [00:08<01:54,  5.02img/2021-03-26 17:04:56,549 train.py[line:353] INFO: Train step_320: loss : 28163.509765625,loss xy : 17.097339630126953,loss wh : 1.4125291109085083,loss obj : 28123.255859375，loss cls : 21.744333267211914,loss l2 : 9708.9140625,lr : 1.0485760000000001e-05\n",
            "Epoch 2/50:  12%| | 72/606 [00:16<01:44,  5.13img/2021-03-26 17:05:04,390 train.py[line:353] INFO: Train step_340: loss : 26947.6015625,loss xy : 19.434261322021484,loss wh : 3.361175298690796,loss obj : 26898.654296875，loss cls : 26.15306282043457,loss l2 : 9139.3310546875,lr : 1.3363360000000005e-05\n",
            "Epoch 2/50:  18%|▏| 112/606 [00:24<01:36,  5.15img2021-03-26 17:05:12,198 train.py[line:353] INFO: Train step_360: loss : 25558.78515625,loss xy : 13.369691848754883,loss wh : 0.8188087344169617,loss obj : 25526.478515625，loss cls : 18.117084503173828,loss l2 : 8481.720703125,lr : 1.6796159999999997e-05\n",
            "Epoch 2/50:  25%|▎| 152/606 [00:32<01:27,  5.18img2021-03-26 17:05:20,043 train.py[line:353] INFO: Train step_380: loss : 24513.267578125,loss xy : 24.237598419189453,loss wh : 7.310996055603027,loss obj : 24448.11328125，loss cls : 33.60618591308594,loss l2 : 7989.61083984375,lr : 2.085136e-05\n",
            "Epoch 2/50:  32%|▎| 192/606 [00:39<01:21,  5.10img2021-03-26 17:05:27,958 train.py[line:353] INFO: Train step_400: loss : 22352.029296875,loss xy : 8.207716941833496,loss wh : 0.9967396259307861,loss obj : 22332.552734375，loss cls : 10.271288871765137,loss l2 : 6982.2548828125,lr : 2.5600000000000006e-05\n",
            "Epoch 2/50:  38%|▍| 232/606 [00:47<01:14,  5.05img2021-03-26 17:05:35,872 train.py[line:353] INFO: Train step_420: loss : 21120.287109375,loss xy : 11.557832717895508,loss wh : 0.6997402906417847,loss obj : 21093.916015625，loss cls : 14.113617897033691,loss l2 : 6417.7685546875,lr : 3.111696e-05\n",
            "Epoch 2/50:  45%|▍| 272/606 [00:55<01:06,  5.05img2021-03-26 17:05:43,812 train.py[line:353] INFO: Train step_440: loss : 19092.26171875,loss xy : 8.467512130737305,loss wh : 1.5786265134811401,loss obj : 19072.02734375，loss cls : 10.18731689453125,loss l2 : 5499.39794921875,lr : 3.748096e-05\n",
            "Epoch 2/50:  51%|▌| 312/606 [01:03<00:58,  5.04img2021-03-26 17:05:51,667 train.py[line:353] INFO: Train step_460: loss : 17866.630859375,loss xy : 5.7275390625,loss wh : 0.4074786305427551,loss obj : 17853.0390625，loss cls : 7.456366539001465,loss l2 : 4947.84619140625,lr : 4.477456000000001e-05\n",
            "Epoch 2/50:  58%|▌| 352/606 [01:11<00:49,  5.11img2021-03-26 17:05:59,570 train.py[line:353] INFO: Train step_480: loss : 16259.6474609375,loss xy : 8.342164993286133,loss wh : 0.4899320900440216,loss obj : 16242.1279296875，loss cls : 8.687185287475586,loss l2 : 4262.61328125,lr : 5.308415999999999e-05\n",
            "Epoch 2/50:  65%|▋| 392/606 [01:19<00:41,  5.12img2021-03-26 17:06:07,406 train.py[line:353] INFO: Train step_500: loss : 14942.646484375,loss xy : 8.470497131347656,loss wh : 1.3335464000701904,loss obj : 14922.7744140625，loss cls : 10.06801700592041,loss l2 : 3710.6318359375,lr : 6.25e-05\n",
            "Epoch 2/50:  71%|▋| 432/606 [01:27<00:34,  5.03img2021-03-26 17:06:15,343 train.py[line:353] INFO: Train step_520: loss : 13954.2001953125,loss xy : 21.536222457885742,loss wh : 2.4458513259887695,loss obj : 13904.94140625，loss cls : 25.276073455810547,loss l2 : 3308.49853515625,lr : 7.311616000000002e-05\n",
            "Epoch 2/50:  78%|▊| 472/606 [01:35<00:26,  5.08img2021-03-26 17:06:23,192 train.py[line:353] INFO: Train step_540: loss : 13130.044921875,loss xy : 16.87432861328125,loss wh : 1.287703037261963,loss obj : 13092.89453125，loss cls : 18.988529205322266,loss l2 : 2996.670166015625,lr : 8.503056000000002e-05\n",
            "Epoch 2/50:  84%|▊| 512/606 [01:43<00:18,  5.13img2021-03-26 17:06:31,002 train.py[line:353] INFO: Train step_560: loss : 12191.9716796875,loss xy : 14.655572891235352,loss wh : 1.0749033689498901,loss obj : 12159.6533203125，loss cls : 16.58747673034668,loss l2 : 2635.897216796875,lr : 9.834496000000003e-05\n",
            "Epoch 2/50:  91%|▉| 552/606 [01:50<00:10,  5.10img2021-03-26 17:06:38,856 train.py[line:353] INFO: Train step_580: loss : 11496.0263671875,loss xy : 25.340560913085938,loss wh : 1.90410315990448,loss obj : 11442.154296875，loss cls : 26.626956939697266,loss l2 : 2388.9462890625,lr : 0.00011316495999999998\n",
            "Epoch 2/50:  98%|▉| 592/606 [01:58<00:02,  5.24img2021-03-26 17:06:46,607 train.py[line:353] INFO: Train step_600: loss : 10583.3359375,loss xy : 22.36338233947754,loss wh : 3.1233088970184326,loss obj : 10533.0830078125，loss cls : 24.76637077331543,loss l2 : 2065.7216796875,lr : 0.0001296\n",
            "Epoch 2/50: 100%|█| 606/606 [02:01<00:00,  5.25img2021-03-26 17:06:49,445 train.py[line:364] INFO: Checkpoint 2 saved !\n",
            "Epoch 2/50: 100%|█| 606/606 [02:01<00:00,  4.97img\n",
            "Epoch 3/50:   4%| | 26/606 [00:07<02:02,  4.74img/2021-03-26 17:06:57,358 train.py[line:353] INFO: Train step_620: loss : 9785.7939453125,loss xy : 10.56661605834961,loss wh : 0.7085493803024292,loss obj : 9764.181640625，loss cls : 10.337264060974121,loss l2 : 1806.2491455078125,lr : 0.00014776335999999997\n",
            "Epoch 3/50:  11%| | 66/606 [00:15<01:45,  5.12img/2021-03-26 17:07:05,233 train.py[line:353] INFO: Train step_640: loss : 9120.5263671875,loss xy : 19.60106658935547,loss wh : 5.412775993347168,loss obj : 9075.2412109375，loss cls : 20.271074295043945,loss l2 : 1601.9039306640625,lr : 0.00016777216000000002\n",
            "Epoch 3/50:  17%|▏| 106/606 [00:23<01:38,  5.09img2021-03-26 17:07:13,115 train.py[line:353] INFO: Train step_660: loss : 8405.2314453125,loss xy : 11.119165420532227,loss wh : 1.0152844190597534,loss obj : 8383.6552734375，loss cls : 9.441585540771484,loss l2 : 1380.6995849609375,lr : 0.00018974736000000004\n",
            "Epoch 3/50:  24%|▏| 146/606 [00:31<01:30,  5.08img2021-03-26 17:07:21,014 train.py[line:353] INFO: Train step_680: loss : 7516.35498046875,loss xy : 8.433749198913574,loss wh : 0.6877440810203552,loss obj : 7498.92626953125，loss cls : 8.30689811706543,loss l2 : 1128.6112060546875,lr : 0.0002138137600000001\n",
            "Epoch 3/50:  31%|▎| 186/606 [00:39<01:23,  5.04img2021-03-26 17:07:29,018 train.py[line:353] INFO: Train step_700: loss : 6826.75537109375,loss xy : 22.108922958374023,loss wh : 6.6017165184021,loss obj : 6782.27392578125，loss cls : 15.771195411682129,loss l2 : 961.5762939453125,lr : 0.00024009999999999995\n",
            "Epoch 3/50:  37%|▎| 226/606 [00:47<01:14,  5.08img2021-03-26 17:07:36,901 train.py[line:353] INFO: Train step_720: loss : 6151.369140625,loss xy : 22.221263885498047,loss wh : 1.0309250354766846,loss obj : 6109.98974609375，loss cls : 18.127674102783203,loss l2 : 789.3297119140625,lr : 0.00026873855999999996\n",
            "Epoch 3/50:  44%|▍| 266/606 [00:54<01:06,  5.15img2021-03-26 17:07:44,736 train.py[line:353] INFO: Train step_740: loss : 5455.28759765625,loss xy : 8.340295791625977,loss wh : 0.541944146156311,loss obj : 5440.57861328125，loss cls : 5.82645845413208,loss l2 : 629.5527954101562,lr : 0.00029986575999999996\n",
            "Epoch 3/50:  50%|▌| 306/606 [01:02<00:57,  5.19img2021-03-26 17:07:52,559 train.py[line:353] INFO: Train step_760: loss : 4898.10400390625,loss xy : 11.368422508239746,loss wh : 0.5980563163757324,loss obj : 4878.1787109375，loss cls : 7.958808422088623,loss l2 : 519.0950317382812,lr : 0.00033362176\n",
            "Epoch 3/50:  57%|▌| 346/606 [01:10<00:50,  5.10img2021-03-26 17:08:00,376 train.py[line:353] INFO: Train step_780: loss : 4328.0986328125,loss xy : 5.562337398529053,loss wh : 0.43206778168678284,loss obj : 4316.79541015625，loss cls : 5.308781147003174,loss l2 : 410.643310546875,lr : 0.00037015056000000006\n",
            "Epoch 3/50:  64%|▋| 386/606 [01:18<00:42,  5.14img2021-03-26 17:08:08,199 train.py[line:353] INFO: Train step_800: loss : 3810.559326171875,loss xy : 8.10525894165039,loss wh : 0.406236469745636,loss obj : 3796.0263671875，loss cls : 6.021368980407715,loss l2 : 322.421630859375,lr : 0.0004096000000000001\n",
            "Epoch 3/50:  70%|▋| 426/606 [01:26<00:35,  5.09img2021-03-26 17:08:16,031 train.py[line:353] INFO: Train step_820: loss : 3359.710693359375,loss xy : 8.077997207641602,loss wh : 0.5631148815155029,loss obj : 3343.30419921875，loss cls : 7.765345096588135,loss l2 : 255.14578247070312,lr : 0.0004521217599999999\n",
            "Epoch 3/50:  77%|▊| 466/606 [01:33<00:27,  5.15img2021-03-26 17:08:23,826 train.py[line:353] INFO: Train step_840: loss : 2958.3564453125,loss xy : 8.01392936706543,loss wh : 0.20303785800933838,loss obj : 2946.300537109375，loss cls : 3.8389875888824463,loss l2 : 199.16363525390625,lr : 0.00049787136\n",
            "Epoch 3/50:  83%|▊| 506/606 [01:41<00:19,  5.09img2021-03-26 17:08:31,675 train.py[line:353] INFO: Train step_860: loss : 2736.27392578125,loss xy : 25.176284790039062,loss wh : 2.694845676422119,loss obj : 2690.185791015625，loss cls : 18.21704864501953,loss l2 : 184.287841796875,lr : 0.00054700816\n",
            "Epoch 3/50:  90%|▉| 546/606 [01:49<00:11,  5.09img2021-03-26 17:08:39,490 train.py[line:353] INFO: Train step_880: loss : 2320.068603515625,loss xy : 13.173624992370605,loss wh : 0.5776644349098206,loss obj : 2293.833251953125，loss cls : 12.484081268310547,loss l2 : 131.54721069335938,lr : 0.00059969536\n",
            "Epoch 3/50:  97%|▉| 586/606 [01:57<00:03,  5.23img2021-03-26 17:08:47,297 train.py[line:353] INFO: Train step_900: loss : 2016.52294921875,loss xy : 9.062850952148438,loss wh : 0.5363832712173462,loss obj : 2000.3466796875，loss cls : 6.5770721435546875,loss l2 : 100.218505859375,lr : 0.0006561000000000001\n",
            "Epoch 3/50: 100%|█| 606/606 [02:01<00:00,  5.25img2021-03-26 17:08:51,266 train.py[line:364] INFO: Checkpoint 3 saved !\n",
            "Epoch 3/50: 100%|█| 606/606 [02:01<00:00,  4.97img\n",
            "Epoch 4/50:   3%| | 20/606 [00:06<02:18,  4.23img/2021-03-26 17:08:57,764 train.py[line:353] INFO: Train step_920: loss : 1734.26513671875,loss xy : 11.084903717041016,loss wh : 1.949860692024231,loss obj : 1712.9468994140625，loss cls : 8.283482551574707,loss l2 : 78.99129486083984,lr : 0.0007163929600000001\n",
            "Epoch 4/50:  10%| | 60/606 [00:13<01:48,  5.02img/2021-03-26 17:09:05,661 train.py[line:353] INFO: Train step_940: loss : 1529.183349609375,loss xy : 7.929607391357422,loss wh : 0.6703906655311584,loss obj : 1512.372314453125，loss cls : 8.211094856262207,loss l2 : 59.62943649291992,lr : 0.0007807489599999998\n",
            "Epoch 4/50:  17%|▏| 100/606 [00:21<01:39,  5.10img2021-03-26 17:09:13,518 train.py[line:353] INFO: Train step_960: loss : 1363.160400390625,loss xy : 22.36339569091797,loss wh : 1.5932855606079102,loss obj : 1328.727783203125，loss cls : 10.475991249084473,loss l2 : 54.38285827636719,lr : 0.0008493465599999999\n",
            "Epoch 4/50:  23%|▏| 140/606 [00:29<01:31,  5.12img2021-03-26 17:09:21,369 train.py[line:353] INFO: Train step_980: loss : 1219.8846435546875,loss xy : 28.35357093811035,loss wh : 2.6681723594665527,loss obj : 1170.157470703125，loss cls : 18.70545196533203,loss l2 : 53.3194465637207,lr : 0.0009223681599999999\n",
            "Epoch 4/50:  30%|▎| 180/606 [00:37<01:24,  5.07img2021-03-26 17:09:29,269 train.py[line:353] INFO: Train step_1000: loss : 1008.1344604492188,loss xy : 7.898641586303711,loss wh : 1.291825294494629,loss obj : 997.4984741210938，loss cls : 1.4455028772354126,loss l2 : 29.007617950439453,lr : 0.001\n",
            "Epoch 4/50:  36%|▎| 220/606 [00:45<01:14,  5.15img2021-03-26 17:09:37,111 train.py[line:353] INFO: Train step_1020: loss : 900.9993286132812,loss xy : 5.355373382568359,loss wh : 0.3383018970489502,loss obj : 890.2064819335938，loss cls : 5.099157333374023,loss l2 : 23.392391204833984,lr : 0.001\n",
            "Epoch 4/50:  43%|▍| 260/606 [00:53<01:08,  5.07img2021-03-26 17:09:44,975 train.py[line:353] INFO: Train step_1040: loss : 796.7705688476562,loss xy : 8.387805938720703,loss wh : 0.3555763363838196,loss obj : 783.0030517578125，loss cls : 5.024094581604004,loss l2 : 19.78096580505371,lr : 0.001\n",
            "Epoch 4/50:  50%|▍| 300/606 [01:01<01:00,  5.10img2021-03-26 17:09:52,803 train.py[line:353] INFO: Train step_1060: loss : 708.716552734375,loss xy : 8.041702270507812,loss wh : 0.27353423833847046,loss obj : 699.803955078125，loss cls : 0.5973771810531616,loss l2 : 15.106130599975586,lr : 0.001\n",
            "Epoch 4/50:  56%|▌| 340/606 [01:08<00:51,  5.18img2021-03-26 17:10:00,610 train.py[line:353] INFO: Train step_1080: loss : 704.7698974609375,loss xy : 22.783924102783203,loss wh : 6.455103397369385,loss obj : 657.492431640625，loss cls : 18.038421630859375,loss l2 : 37.65402603149414,lr : 0.001\n",
            "Epoch 4/50:  63%|▋| 380/606 [01:16<00:44,  5.08img2021-03-26 17:10:08,450 train.py[line:353] INFO: Train step_1100: loss : 605.857421875,loss xy : 5.092771530151367,loss wh : 0.19365927577018738,loss obj : 597.307861328125，loss cls : 3.263124942779541,loss l2 : 12.035491943359375,lr : 0.001\n",
            "Epoch 4/50:  69%|▋| 420/606 [01:24<00:36,  5.08img2021-03-26 17:10:16,313 train.py[line:353] INFO: Train step_1120: loss : 562.7275390625,loss xy : 10.94675350189209,loss wh : 1.4564175605773926,loss obj : 544.2532958984375，loss cls : 6.071018218994141,loss l2 : 16.300554275512695,lr : 0.001\n",
            "Epoch 4/50:  76%|▊| 460/606 [01:32<00:28,  5.06img2021-03-26 17:10:24,181 train.py[line:353] INFO: Train step_1140: loss : 500.1687927246094,loss xy : 5.400468826293945,loss wh : 0.6311547756195068,loss obj : 493.43731689453125，loss cls : 0.699851393699646,loss l2 : 9.1926851272583,lr : 0.001\n",
            "Epoch 4/50:  83%|▊| 500/606 [01:40<00:20,  5.10img2021-03-26 17:10:32,070 train.py[line:353] INFO: Train step_1160: loss : 466.5206604003906,loss xy : 5.4307861328125,loss wh : 0.6643266677856445,loss obj : 460.0274963378906，loss cls : 0.39805567264556885,loss l2 : 8.458566665649414,lr : 0.001\n",
            "Epoch 4/50:  89%|▉| 540/606 [01:48<00:12,  5.09img2021-03-26 17:10:39,893 train.py[line:353] INFO: Train step_1180: loss : 443.4079284667969,loss xy : 11.706768035888672,loss wh : 0.7635958790779114,loss obj : 426.0516357421875，loss cls : 4.885926246643066,loss l2 : 11.82030963897705,lr : 0.001\n",
            "Epoch 4/50:  96%|▉| 580/606 [01:56<00:05,  5.19img2021-03-26 17:10:47,726 train.py[line:353] INFO: Train step_1200: loss : 435.17620849609375,loss xy : 17.66196060180664,loss wh : 2.1158957481384277,loss obj : 407.5429992675781，loss cls : 7.8553547859191895,loss l2 : 17.907154083251953,lr : 0.001\n",
            "Epoch 4/50: 100%|█| 606/606 [02:01<00:00,  5.20img2021-03-26 17:10:53,052 train.py[line:364] INFO: Checkpoint 4 saved !\n",
            "Epoch 4/50: 100%|█| 606/606 [02:01<00:00,  4.98img\n",
            "Epoch 5/50:   2%| | 14/606 [00:05<03:22,  2.93img/2021-03-26 17:10:58,703 train.py[line:353] INFO: Train step_1220: loss : 376.0762939453125,loss xy : 6.315232753753662,loss wh : 0.3511902093887329,loss obj : 366.43719482421875，loss cls : 2.972698211669922,loss l2 : 7.2184576988220215,lr : 0.001\n",
            "Epoch 5/50:   9%| | 54/606 [00:13<01:48,  5.08img/2021-03-26 17:11:06,602 train.py[line:353] INFO: Train step_1240: loss : 391.3257751464844,loss xy : 16.535730361938477,loss wh : 5.074893474578857,loss obj : 358.47418212890625，loss cls : 11.240968704223633,loss l2 : 22.992965698242188,lr : 0.001\n",
            "Epoch 5/50:  16%|▏| 94/606 [00:21<01:40,  5.07img/2021-03-26 17:11:14,476 train.py[line:353] INFO: Train step_1260: loss : 337.3892822265625,loss xy : 8.181278228759766,loss wh : 0.27434539794921875,loss obj : 325.53790283203125，loss cls : 3.395759105682373,loss l2 : 7.21999454498291,lr : 0.001\n",
            "Epoch 5/50:  22%|▏| 134/606 [00:28<01:32,  5.10img2021-03-26 17:11:22,362 train.py[line:353] INFO: Train step_1280: loss : 317.4163818359375,loss xy : 5.512794494628906,loss wh : 0.3558904528617859,loss obj : 308.0069580078125，loss cls : 3.5407397747039795,loss l2 : 6.0375261306762695,lr : 0.001\n",
            "Epoch 5/50:  29%|▎| 174/606 [00:36<01:24,  5.09img2021-03-26 17:11:30,233 train.py[line:353] INFO: Train step_1300: loss : 353.0589904785156,loss xy : 23.242427825927734,loss wh : 1.3304699659347534,loss obj : 316.44281005859375，loss cls : 12.043277740478516,loss l2 : 17.93572425842285,lr : 0.001\n",
            "Epoch 5/50:  35%|▎| 214/606 [00:44<01:17,  5.08img2021-03-26 17:11:38,098 train.py[line:353] INFO: Train step_1320: loss : 321.1941223144531,loss xy : 16.7251033782959,loss wh : 7.796002388000488,loss obj : 290.2312316894531，loss cls : 6.4417595863342285,loss l2 : 25.840190887451172,lr : 0.001\n",
            "Epoch 5/50:  42%|▍| 254/606 [00:52<01:08,  5.10img2021-03-26 17:11:45,941 train.py[line:353] INFO: Train step_1340: loss : 321.9218444824219,loss xy : 22.081653594970703,loss wh : 2.2367665767669678,loss obj : 285.8648376464844，loss cls : 11.73858642578125,loss l2 : 18.348299026489258,lr : 0.001\n",
            "Epoch 5/50:  49%|▍| 294/606 [01:00<01:00,  5.18img2021-03-26 17:11:53,747 train.py[line:353] INFO: Train step_1360: loss : 269.3388366699219,loss xy : 11.142396926879883,loss wh : 1.562247633934021,loss obj : 255.12551879882812，loss cls : 1.5086524486541748,loss l2 : 9.133414268493652,lr : 0.001\n",
            "Epoch 5/50:  55%|▌| 334/606 [01:08<00:52,  5.15img2021-03-26 17:12:01,576 train.py[line:353] INFO: Train step_1380: loss : 272.1505432128906,loss xy : 14.742349624633789,loss wh : 1.3517194986343384,loss obj : 249.18719482421875，loss cls : 6.8692851066589355,loss l2 : 11.887163162231445,lr : 0.001\n",
            "Epoch 5/50:  62%|▌| 374/606 [01:15<00:44,  5.17img2021-03-26 17:12:09,406 train.py[line:353] INFO: Train step_1400: loss : 316.9764099121094,loss xy : 30.559335708618164,loss wh : 3.8253347873687744,loss obj : 263.32562255859375，loss cls : 19.26610565185547,loss l2 : 26.40515899658203,lr : 0.001\n",
            "Epoch 5/50:  68%|▋| 414/606 [01:23<00:37,  5.08img2021-03-26 17:12:17,296 train.py[line:353] INFO: Train step_1420: loss : 240.50552368164062,loss xy : 11.195586204528809,loss wh : 1.5813374519348145,loss obj : 222.86553955078125，loss cls : 4.863075256347656,loss l2 : 10.1332426071167,lr : 0.001\n",
            "Epoch 5/50:  75%|▋| 454/606 [01:31<00:29,  5.12img2021-03-26 17:12:25,154 train.py[line:353] INFO: Train step_1440: loss : 250.00973510742188,loss xy : 16.636493682861328,loss wh : 1.2411693334579468,loss obj : 223.06942749023438，loss cls : 9.062653541564941,loss l2 : 12.985116004943848,lr : 0.001\n",
            "Epoch 5/50:  82%|▊| 494/606 [01:39<00:21,  5.13img2021-03-26 17:12:32,982 train.py[line:353] INFO: Train step_1460: loss : 230.72842407226562,loss xy : 11.148259162902832,loss wh : 1.1319416761398315,loss obj : 205.08740234375，loss cls : 13.360816955566406,loss l2 : 12.080289840698242,lr : 0.001\n",
            "Epoch 5/50:  88%|▉| 534/606 [01:47<00:14,  5.12img2021-03-26 17:12:40,839 train.py[line:353] INFO: Train step_1480: loss : 236.0443878173828,loss xy : 16.825210571289062,loss wh : 1.7126224040985107,loss obj : 208.2539825439453，loss cls : 9.252580642700195,loss l2 : 14.052581787109375,lr : 0.001\n",
            "Epoch 5/50:  95%|▉| 574/606 [01:55<00:06,  5.12img2021-03-26 17:12:48,668 train.py[line:353] INFO: Train step_1500: loss : 195.62379455566406,loss xy : 8.078115463256836,loss wh : 0.12446556240320206,loss obj : 184.16616821289062，loss cls : 3.2550573348999023,loss l2 : 5.267759323120117,lr : 0.001\n",
            "Epoch 5/50: 100%|█| 606/606 [02:01<00:00,  5.22img2021-03-26 17:12:54,985 train.py[line:364] INFO: Checkpoint 5 saved !\n",
            "Epoch 5/50: 100%|█| 606/606 [02:01<00:00,  4.97img\n",
            "Epoch 6/50:   1%| | 8/606 [00:03<05:04,  1.96img/s2021-03-26 17:12:59,126 train.py[line:353] INFO: Train step_1520: loss : 248.63128662109375,loss xy : 25.27509307861328,loss wh : 1.4358222484588623,loss obj : 206.51695251464844，loss cls : 15.403414726257324,loss l2 : 18.65007781982422,lr : 0.001\n",
            "Epoch 6/50:   8%| | 48/606 [00:11<01:49,  5.09img/2021-03-26 17:13:06,940 train.py[line:353] INFO: Train step_1540: loss : 218.0895538330078,loss xy : 16.60801124572754,loss wh : 6.451624393463135,loss obj : 184.2899932861328，loss cls : 10.739927291870117,loss l2 : 23.479793548583984,lr : 0.001\n",
            "Epoch 6/50:  15%|▏| 88/606 [00:19<01:42,  5.03img/2021-03-26 17:13:14,864 train.py[line:353] INFO: Train step_1560: loss : 168.82281494140625,loss xy : 5.384815216064453,loss wh : 0.08587980270385742,loss obj : 162.16058349609375，loss cls : 1.1915379762649536,loss l2 : 2.791318416595459,lr : 0.001\n",
            "Epoch 6/50:  21%|▏| 128/606 [00:27<01:34,  5.04img2021-03-26 17:13:22,757 train.py[line:353] INFO: Train step_1580: loss : 198.8724365234375,loss xy : 17.381052017211914,loss wh : 2.5521292686462402,loss obj : 171.77365112304688，loss cls : 7.165598392486572,loss l2 : 14.501073837280273,lr : 0.001\n",
            "Epoch 6/50:  28%|▎| 168/606 [00:35<01:26,  5.07img2021-03-26 17:13:30,622 train.py[line:353] INFO: Train step_1600: loss : 233.80178833007812,loss xy : 30.49210548400879,loss wh : 1.7380359172821045,loss obj : 193.5156707763672，loss cls : 8.055964469909668,loss l2 : 17.827564239501953,lr : 0.001\n",
            "Epoch 6/50:  34%|▎| 208/606 [00:43<01:19,  5.03img2021-03-26 17:13:38,496 train.py[line:353] INFO: Train step_1620: loss : 204.10494995117188,loss xy : 21.584712982177734,loss wh : 1.354244351387024,loss obj : 172.58470153808594，loss cls : 8.581303596496582,loss l2 : 15.392548561096191,lr : 0.001\n",
            "Epoch 6/50:  41%|▍| 248/606 [00:50<01:10,  5.10img2021-03-26 17:13:46,307 train.py[line:353] INFO: Train step_1640: loss : 155.48883056640625,loss xy : 7.905954360961914,loss wh : 0.18390150368213654,loss obj : 139.99388122558594，loss cls : 7.405084609985352,loss l2 : 6.377595901489258,lr : 0.001\n",
            "Epoch 6/50:  48%|▍| 288/606 [00:58<01:01,  5.14img2021-03-26 17:13:54,139 train.py[line:353] INFO: Train step_1660: loss : 168.198486328125,loss xy : 11.230433464050293,loss wh : 0.5342673063278198,loss obj : 144.66574096679688，loss cls : 11.768046379089355,loss l2 : 10.746708869934082,lr : 0.001\n",
            "Epoch 6/50:  54%|▌| 328/606 [01:06<00:54,  5.12img2021-03-26 17:14:01,982 train.py[line:353] INFO: Train step_1680: loss : 187.0169219970703,loss xy : 19.47265625,loss wh : 1.4332717657089233,loss obj : 156.47952270507812，loss cls : 9.631471633911133,loss l2 : 15.244136810302734,lr : 0.001\n",
            "Epoch 6/50:  61%|▌| 368/606 [01:14<00:46,  5.10img2021-03-26 17:14:09,832 train.py[line:353] INFO: Train step_1700: loss : 160.03805541992188,loss xy : 13.443105697631836,loss wh : 1.4745852947235107,loss obj : 137.94589233398438，loss cls : 7.174469947814941,loss l2 : 10.457359313964844,lr : 0.001\n",
            "Epoch 6/50:  67%|▋| 408/606 [01:22<00:38,  5.13img2021-03-26 17:14:17,667 train.py[line:353] INFO: Train step_1720: loss : 138.90093994140625,loss xy : 8.211118698120117,loss wh : 0.21982941031455994,loss obj : 125.35636138916016，loss cls : 5.113638877868652,loss l2 : 5.6680006980896,lr : 0.001\n",
            "Epoch 6/50:  74%|▋| 448/606 [01:30<00:30,  5.12img2021-03-26 17:14:25,529 train.py[line:353] INFO: Train step_1740: loss : 157.46456909179688,loss xy : 16.5283203125,loss wh : 1.1133323907852173,loss obj : 133.28839111328125，loss cls : 6.5345377922058105,loss l2 : 10.770280838012695,lr : 0.001\n",
            "Epoch 6/50:  81%|▊| 488/606 [01:37<00:23,  5.10img2021-03-26 17:14:33,378 train.py[line:353] INFO: Train step_1760: loss : 148.9334259033203,loss xy : 13.484956741333008,loss wh : 2.0368099212646484,loss obj : 128.77191162109375，loss cls : 4.6397504806518555,loss l2 : 11.072154998779297,lr : 0.001\n",
            "Epoch 6/50:  87%|▊| 528/606 [01:45<00:15,  5.09img2021-03-26 17:14:41,233 train.py[line:353] INFO: Train step_1780: loss : 219.1930694580078,loss xy : 32.7023811340332,loss wh : 15.768584251403809,loss obj : 159.87261962890625，loss cls : 10.849485397338867,loss l2 : 48.5582275390625,lr : 0.001\n",
            "Epoch 6/50:  94%|▉| 568/606 [01:53<00:07,  5.14img2021-03-26 17:14:49,069 train.py[line:353] INFO: Train step_1800: loss : 136.00340270996094,loss xy : 11.6368989944458,loss wh : 2.2396445274353027,loss obj : 117.90997314453125，loss cls : 4.216882705688477,loss l2 : 10.751288414001465,lr : 0.001\n",
            "Epoch 6/50: 100%|█| 606/606 [02:00<00:00,  5.23img2021-03-26 17:14:56,562 train.py[line:364] INFO: Checkpoint 6 saved !\n",
            "Epoch 6/50: 100%|█| 606/606 [02:01<00:00,  4.98img\n",
            "Epoch 7/50:   0%| | 2/606 [00:02<14:29,  1.44s/img2021-03-26 17:14:59,917 train.py[line:353] INFO: Train step_1820: loss : 138.6457061767578,loss xy : 13.75374698638916,loss wh : 1.6641418933868408,loss obj : 112.92584991455078，loss cls : 10.301965713500977,loss l2 : 12.300151824951172,lr : 0.001\n",
            "Epoch 7/50:   7%| | 42/606 [00:10<01:51,  5.05img/2021-03-26 17:15:07,797 train.py[line:353] INFO: Train step_1840: loss : 113.11215209960938,loss xy : 8.232050895690918,loss wh : 0.09259691089391708,loss obj : 103.65216827392578，loss cls : 1.1353399753570557,loss l2 : 3.4614367485046387,lr : 0.001\n",
            "Epoch 7/50:  14%|▏| 82/606 [00:18<01:42,  5.11img/2021-03-26 17:15:15,680 train.py[line:353] INFO: Train step_1860: loss : 151.32391357421875,loss xy : 19.24161720275879,loss wh : 1.2984106540679932,loss obj : 120.88662719726562，loss cls : 9.897259712219238,loss l2 : 13.506895065307617,lr : 0.001\n",
            "Epoch 7/50:  20%|▏| 122/606 [00:26<01:34,  5.11img2021-03-26 17:15:23,538 train.py[line:353] INFO: Train step_1880: loss : 136.12408447265625,loss xy : 16.438020706176758,loss wh : 0.8688993453979492,loss obj : 114.01004791259766，loss cls : 4.807116985321045,loss l2 : 10.253946304321289,lr : 0.001\n",
            "Epoch 7/50:  27%|▎| 162/606 [00:34<01:27,  5.07img2021-03-26 17:15:31,411 train.py[line:353] INFO: Train step_1900: loss : 110.60929870605469,loss xy : 8.27150821685791,loss wh : 1.0869444608688354,loss obj : 97.33529663085938，loss cls : 3.915548801422119,loss l2 : 7.339253902435303,lr : 0.001\n",
            "Epoch 7/50:  33%|▎| 202/606 [00:42<01:18,  5.12img2021-03-26 17:15:39,280 train.py[line:353] INFO: Train step_1920: loss : 118.05634307861328,loss xy : 13.259323120117188,loss wh : 0.5885332822799683,loss obj : 99.64161682128906，loss cls : 4.5668745040893555,loss l2 : 7.933104038238525,lr : 0.001\n",
            "Epoch 7/50:  40%|▍| 242/606 [00:50<01:10,  5.14img2021-03-26 17:15:47,089 train.py[line:353] INFO: Train step_1940: loss : 132.03805541992188,loss xy : 15.783539772033691,loss wh : 1.4495733976364136,loss obj : 101.67645263671875，loss cls : 13.128501892089844,loss l2 : 13.458820343017578,lr : 0.001\n",
            "Epoch 7/50:  47%|▍| 282/606 [00:58<01:03,  5.08img2021-03-26 17:15:54,971 train.py[line:353] INFO: Train step_1960: loss : 90.27816772460938,loss xy : 4.112056732177734,loss wh : 0.42046278715133667,loss obj : 82.8782730102539，loss cls : 2.8673758506774902,loss l2 : 4.085509300231934,lr : 0.001\n",
            "Epoch 7/50:  53%|▌| 322/606 [01:05<00:56,  5.07img2021-03-26 17:16:02,895 train.py[line:353] INFO: Train step_1980: loss : 95.48693084716797,loss xy : 5.758526802062988,loss wh : 0.5983448028564453,loss obj : 83.89707946777344，loss cls : 5.232976913452148,loss l2 : 5.632782936096191,lr : 0.001\n",
            "Epoch 7/50:  60%|▌| 362/606 [01:13<00:47,  5.12img2021-03-26 17:16:10,801 train.py[line:353] INFO: Train step_2000: loss : 127.08112335205078,loss xy : 18.5621395111084,loss wh : 0.7794780731201172,loss obj : 100.34878540039062，loss cls : 7.390727519989014,loss l2 : 11.385761260986328,lr : 0.001\n",
            "Epoch 7/50:  66%|▋| 402/606 [01:21<00:40,  5.09img2021-03-26 17:16:18,670 train.py[line:353] INFO: Train step_2020: loss : 85.36579132080078,loss xy : 5.349078178405762,loss wh : 0.593158483505249,loss obj : 75.07600402832031，loss cls : 4.347548961639404,loss l2 : 4.699769973754883,lr : 0.001\n",
            "Epoch 7/50:  73%|▋| 442/606 [01:29<00:32,  5.03img2021-03-26 17:16:26,559 train.py[line:353] INFO: Train step_2040: loss : 117.68824768066406,loss xy : 11.894588470458984,loss wh : 1.4130833148956299,loss obj : 92.46578216552734，loss cls : 11.914796829223633,loss l2 : 11.33669376373291,lr : 0.001\n",
            "Epoch 7/50:  80%|▊| 482/606 [01:37<00:24,  5.10img2021-03-26 17:16:34,434 train.py[line:353] INFO: Train step_2060: loss : 116.16719055175781,loss xy : 15.314814567565918,loss wh : 1.6174720525741577,loss obj : 90.6885757446289，loss cls : 8.546324729919434,loss l2 : 12.751806259155273,lr : 0.001\n",
            "Epoch 7/50:  86%|▊| 522/606 [01:45<00:16,  5.08img2021-03-26 17:16:42,281 train.py[line:353] INFO: Train step_2080: loss : 169.09323120117188,loss xy : 29.18398666381836,loss wh : 8.229562759399414,loss obj : 106.97200012207031，loss cls : 24.70767593383789,loss l2 : 35.663536071777344,lr : 0.001\n",
            "Epoch 7/50:  93%|▉| 562/606 [01:53<00:08,  5.07img2021-03-26 17:16:50,214 train.py[line:353] INFO: Train step_2100: loss : 123.9729232788086,loss xy : 18.61489486694336,loss wh : 1.4261072874069214,loss obj : 92.39607238769531，loss cls : 11.535852432250977,loss l2 : 13.734076499938965,lr : 0.001\n",
            "Epoch 7/50:  99%|▉| 602/606 [02:00<00:00,  5.23img2021-03-26 17:16:57,937 train.py[line:353] INFO: Train step_2120: loss : 96.572998046875,loss xy : 10.330317497253418,loss wh : 1.2976083755493164,loss obj : 78.89057922363281，loss cls : 6.054485321044922,loss l2 : 9.013127326965332,lr : 0.001\n",
            "Epoch 7/50: 100%|█| 606/606 [02:01<00:00,  5.22img2021-03-26 17:16:58,846 train.py[line:364] INFO: Checkpoint 7 saved !\n",
            "Epoch 7/50: 100%|█| 606/606 [02:02<00:00,  4.96img\n",
            "Epoch 8/50:   6%| | 36/606 [00:09<01:51,  5.09img/2021-03-26 17:17:08,630 train.py[line:353] INFO: Train step_2140: loss : 76.03895568847656,loss xy : 5.670809745788574,loss wh : 0.3397477865219116,loss obj : 67.69953155517578，loss cls : 2.3288686275482178,loss l2 : 3.5370900630950928,lr : 0.001\n",
            "Epoch 8/50:  13%|▏| 76/606 [00:17<01:43,  5.12img/2021-03-26 17:17:16,437 train.py[line:353] INFO: Train step_2160: loss : 82.9637680053711,loss xy : 9.192367553710938,loss wh : 0.3691776394844055,loss obj : 67.42493438720703，loss cls : 5.977285861968994,loss l2 : 5.5272626876831055,lr : 0.001\n",
            "Epoch 8/50:  19%|▏| 116/606 [00:24<01:34,  5.16img2021-03-26 17:17:24,227 train.py[line:353] INFO: Train step_2180: loss : 98.96267700195312,loss xy : 12.176336288452148,loss wh : 0.6054272651672363,loss obj : 78.01670837402344，loss cls : 8.164209365844727,loss l2 : 9.764464378356934,lr : 0.001\n",
            "Epoch 8/50:  26%|▎| 156/606 [00:32<01:29,  5.01img2021-03-26 17:17:32,127 train.py[line:353] INFO: Train step_2200: loss : 89.97896575927734,loss xy : 11.624692916870117,loss wh : 0.7269237637519836,loss obj : 73.9976806640625，loss cls : 3.629669427871704,loss l2 : 7.022355556488037,lr : 0.001\n",
            "Epoch 8/50:  32%|▎| 196/606 [00:40<01:20,  5.10img2021-03-26 17:17:39,944 train.py[line:353] INFO: Train step_2220: loss : 92.2816390991211,loss xy : 14.680542945861816,loss wh : 0.9708949327468872,loss obj : 70.25984191894531，loss cls : 6.370364189147949,loss l2 : 8.811071395874023,lr : 0.001\n",
            "Epoch 8/50:  39%|▍| 236/606 [00:48<01:11,  5.15img2021-03-26 17:17:47,696 train.py[line:353] INFO: Train step_2240: loss : 67.62297058105469,loss xy : 8.060831069946289,loss wh : 0.14363835752010345,loss obj : 58.39115524291992，loss cls : 1.0273411273956299,loss l2 : 2.0371458530426025,lr : 0.001\n",
            "Epoch 8/50:  46%|▍| 276/606 [00:56<01:05,  5.06img2021-03-26 17:17:55,551 train.py[line:353] INFO: Train step_2260: loss : 92.52285766601562,loss xy : 14.646445274353027,loss wh : 0.977233350276947,loss obj : 69.004638671875，loss cls : 7.8945417404174805,loss l2 : 9.076964378356934,lr : 0.001\n",
            "Epoch 8/50:  52%|▌| 316/606 [01:04<00:57,  5.08img2021-03-26 17:18:03,448 train.py[line:353] INFO: Train step_2280: loss : 116.28128051757812,loss xy : 21.61846160888672,loss wh : 1.010412573814392,loss obj : 75.65608978271484，loss cls : 17.996318817138672,loss l2 : 15.552802085876465,lr : 0.001\n",
            "Epoch 8/50:  59%|▌| 356/606 [01:12<00:48,  5.11img2021-03-26 17:18:11,337 train.py[line:353] INFO: Train step_2300: loss : 141.09605407714844,loss xy : 29.282825469970703,loss wh : 15.817231178283691,loss obj : 88.76171875，loss cls : 7.234289169311523,loss l2 : 43.174964904785156,lr : 0.001\n",
            "Epoch 8/50:  65%|▋| 396/606 [01:19<00:41,  5.12img2021-03-26 17:18:19,188 train.py[line:353] INFO: Train step_2320: loss : 69.97688293457031,loss xy : 7.95674991607666,loss wh : 0.5290744304656982,loss obj : 55.72315979003906，loss cls : 5.7678985595703125,loss l2 : 5.416193008422852,lr : 0.001\n",
            "Epoch 8/50:  72%|▋| 436/606 [01:27<00:33,  5.15img2021-03-26 17:18:26,999 train.py[line:353] INFO: Train step_2340: loss : 84.25636291503906,loss xy : 12.757930755615234,loss wh : 0.669752836227417,loss obj : 63.04533386230469，loss cls : 7.783346176147461,loss l2 : 7.940884113311768,lr : 0.001\n",
            "Epoch 8/50:  79%|▊| 476/606 [01:35<00:25,  5.14img2021-03-26 17:18:34,811 train.py[line:353] INFO: Train step_2360: loss : 79.32276153564453,loss xy : 15.480048179626465,loss wh : 1.3931753635406494,loss obj : 60.56498336791992，loss cls : 1.884549617767334,loss l2 : 7.998697280883789,lr : 0.001\n",
            "Epoch 8/50:  85%|▊| 516/606 [01:43<00:17,  5.11img2021-03-26 17:18:42,677 train.py[line:353] INFO: Train step_2380: loss : 84.42453002929688,loss xy : 16.597152709960938,loss wh : 0.36672261357307434,loss obj : 58.55238342285156，loss cls : 8.908271789550781,loss l2 : 6.472522258758545,lr : 0.001\n",
            "Epoch 8/50:  92%|▉| 556/606 [01:51<00:09,  5.18img2021-03-26 17:18:50,465 train.py[line:353] INFO: Train step_2400: loss : 59.191104888916016,loss xy : 5.752120018005371,loss wh : 0.2573668360710144,loss obj : 51.286712646484375，loss cls : 1.8949052095413208,loss l2 : 2.8280694484710693,lr : 0.001\n",
            "Epoch 8/50:  98%|▉| 596/606 [01:58<00:01,  5.24img2021-03-26 17:18:58,197 train.py[line:353] INFO: Train step_2420: loss : 90.61620330810547,loss xy : 15.747010231018066,loss wh : 1.9757177829742432,loss obj : 68.4561767578125，loss cls : 4.437296390533447,loss l2 : 9.938516616821289,lr : 0.001\n",
            "Epoch 8/50: 100%|█| 606/606 [02:00<00:00,  5.21img2021-03-26 17:19:00,257 train.py[line:364] INFO: Checkpoint 8 saved !\n",
            "Epoch 8/50: 100%|█| 606/606 [02:01<00:00,  4.99img\n",
            "Epoch 9/50:   5%| | 30/606 [00:08<01:57,  4.91img/2021-03-26 17:19:08,998 train.py[line:353] INFO: Train step_2440: loss : 97.67984008789062,loss xy : 19.105358123779297,loss wh : 2.601783275604248,loss obj : 63.861053466796875，loss cls : 12.111644744873047,loss l2 : 14.548376083374023,lr : 0.001\n",
            "Epoch 9/50:  12%| | 70/606 [00:16<01:45,  5.06img/2021-03-26 17:19:16,855 train.py[line:353] INFO: Train step_2460: loss : 65.53289031982422,loss xy : 7.620865821838379,loss wh : 0.3814145028591156,loss obj : 51.59691619873047，loss cls : 5.933694362640381,loss l2 : 5.2909393310546875,lr : 0.001\n",
            "Epoch 9/50:  18%|▏| 110/606 [00:24<01:37,  5.07img2021-03-26 17:19:24,755 train.py[line:353] INFO: Train step_2480: loss : 76.0595703125,loss xy : 12.05074691772461,loss wh : 0.6404858231544495,loss obj : 54.356109619140625，loss cls : 9.012229919433594,loss l2 : 8.689417839050293,lr : 0.001\n",
            "Epoch 9/50:  25%|▏| 150/606 [00:31<01:29,  5.09img2021-03-26 17:19:32,633 train.py[line:353] INFO: Train step_2500: loss : 90.60069274902344,loss xy : 14.694851875305176,loss wh : 8.264033317565918,loss obj : 58.05210876464844，loss cls : 9.589698791503906,loss l2 : 23.713504791259766,lr : 0.001\n",
            "Epoch 9/50:  31%|▎| 190/606 [00:39<01:20,  5.15img2021-03-26 17:19:40,504 train.py[line:353] INFO: Train step_2520: loss : 56.460182189941406,loss xy : 4.585697174072266,loss wh : 0.4793764352798462,loss obj : 51.194236755371094，loss cls : 0.20086924731731415,loss l2 : 3.4100747108459473,lr : 0.001\n",
            "Epoch 9/50:  38%|▍| 230/606 [00:47<01:14,  5.07img2021-03-26 17:19:48,346 train.py[line:353] INFO: Train step_2540: loss : 58.80852508544922,loss xy : 7.107872009277344,loss wh : 0.4713106155395508,loss obj : 49.477169036865234，loss cls : 1.7521742582321167,loss l2 : 4.771575450897217,lr : 0.001\n",
            "Epoch 9/50:  45%|▍| 270/606 [00:55<01:06,  5.02img2021-03-26 17:19:56,232 train.py[line:353] INFO: Train step_2560: loss : 94.37127685546875,loss xy : 19.88567543029785,loss wh : 2.133106231689453,loss obj : 64.74525451660156，loss cls : 7.607239246368408,loss l2 : 13.979734420776367,lr : 0.001\n",
            "Epoch 9/50:  51%|▌| 310/606 [01:03<00:57,  5.15img2021-03-26 17:20:04,109 train.py[line:353] INFO: Train step_2580: loss : 94.63851928710938,loss xy : 20.798295974731445,loss wh : 5.763516902923584,loss obj : 58.81694793701172，loss cls : 9.259760856628418,loss l2 : 19.03822135925293,lr : 0.001\n",
            "Epoch 9/50:  58%|▌| 350/606 [01:11<00:51,  5.02img2021-03-26 17:20:11,998 train.py[line:353] INFO: Train step_2600: loss : 86.486083984375,loss xy : 18.847816467285156,loss wh : 1.4555116891860962,loss obj : 56.714820861816406，loss cls : 9.467937469482422,loss l2 : 12.27563762664795,lr : 0.001\n",
            "Epoch 9/50:  64%|▋| 390/606 [01:19<00:42,  5.10img2021-03-26 17:20:19,839 train.py[line:353] INFO: Train step_2620: loss : 117.78337097167969,loss xy : 33.00053405761719,loss wh : 2.1276397705078125,loss obj : 68.97566986083984，loss cls : 13.67952823638916,loss l2 : 18.991172790527344,lr : 0.001\n",
            "Epoch 9/50:  71%|▋| 430/606 [01:27<00:34,  5.05img2021-03-26 17:20:27,719 train.py[line:353] INFO: Train step_2640: loss : 71.81387329101562,loss xy : 13.350292205810547,loss wh : 0.9004092216491699,loss obj : 50.87799835205078，loss cls : 6.685175895690918,loss l2 : 8.269617080688477,lr : 0.001\n",
            "Epoch 9/50:  78%|▊| 470/606 [01:34<00:26,  5.05img2021-03-26 17:20:35,573 train.py[line:353] INFO: Train step_2660: loss : 77.4596939086914,loss xy : 15.033761024475098,loss wh : 1.1439158916473389,loss obj : 51.082298278808594，loss cls : 10.199716567993164,loss l2 : 11.130640983581543,lr : 0.001\n",
            "Epoch 9/50:  84%|▊| 510/606 [01:42<00:18,  5.13img2021-03-26 17:20:43,421 train.py[line:353] INFO: Train step_2680: loss : 50.58698654174805,loss xy : 9.11859130859375,loss wh : 0.46814635396003723,loss obj : 40.13908004760742，loss cls : 0.8611672520637512,loss l2 : 3.1929402351379395,lr : 0.001\n",
            "Epoch 9/50:  91%|▉| 550/606 [01:50<00:10,  5.12img2021-03-26 17:20:51,282 train.py[line:353] INFO: Train step_2700: loss : 68.98577880859375,loss xy : 15.172836303710938,loss wh : 0.7095320820808411,loss obj : 50.26966857910156，loss cls : 2.833739757537842,loss l2 : 7.7565016746521,lr : 0.001\n",
            "Epoch 9/50:  97%|▉| 590/606 [01:58<00:03,  5.22img2021-03-26 17:20:59,084 train.py[line:353] INFO: Train step_2720: loss : 79.07942962646484,loss xy : 11.704891204833984,loss wh : 1.3071212768554688,loss obj : 48.85623550415039，loss cls : 17.211181640625,loss l2 : 11.257529258728027,lr : 0.001\n",
            "Epoch 9/50: 100%|█| 606/606 [02:01<00:00,  5.21img2021-03-26 17:21:02,279 train.py[line:364] INFO: Checkpoint 9 saved !\n",
            "Epoch 9/50: 100%|█| 606/606 [02:02<00:00,  4.97img\n",
            "Epoch 10/50:   4%| | 24/606 [00:07<02:10,  4.44img2021-03-26 17:21:10,104 train.py[line:353] INFO: Train step_2740: loss : 107.48770141601562,loss xy : 25.305892944335938,loss wh : 1.5619401931762695,loss obj : 65.19194793701172，loss cls : 15.427921295166016,loss l2 : 17.521728515625,lr : 0.001\n",
            "Epoch 10/50:  11%| | 64/606 [00:15<01:46,  5.10img2021-03-26 17:21:17,959 train.py[line:353] INFO: Train step_2760: loss : 72.41040802001953,loss xy : 12.860730171203613,loss wh : 1.6701399087905884,loss obj : 53.14530563354492，loss cls : 4.734227657318115,loss l2 : 9.258193016052246,lr : 0.001\n",
            "Epoch 10/50:  17%|▏| 104/606 [00:23<01:39,  5.06im2021-03-26 17:21:25,848 train.py[line:353] INFO: Train step_2780: loss : 60.63194274902344,loss xy : 10.15774154663086,loss wh : 0.6243268847465515,loss obj : 42.25800704956055，loss cls : 7.591866493225098,loss l2 : 8.02646255493164,lr : 0.001\n",
            "Epoch 10/50:  24%|▏| 144/606 [00:31<01:30,  5.09im2021-03-26 17:21:33,773 train.py[line:353] INFO: Train step_2800: loss : 116.43231964111328,loss xy : 27.307117462158203,loss wh : 2.401150941848755,loss obj : 68.48284912109375，loss cls : 18.241201400756836,loss l2 : 21.565649032592773,lr : 0.001\n",
            "Epoch 10/50:  30%|▎| 184/606 [00:38<01:23,  5.07im2021-03-26 17:21:41,658 train.py[line:353] INFO: Train step_2820: loss : 57.072757720947266,loss xy : 6.06923770904541,loss wh : 5.720630645751953,loss obj : 43.08530044555664，loss cls : 2.1975910663604736,loss l2 : 15.059786796569824,lr : 0.001\n",
            "Epoch 10/50:  37%|▎| 224/606 [00:46<01:15,  5.05im2021-03-26 17:21:49,563 train.py[line:353] INFO: Train step_2840: loss : 82.44400024414062,loss xy : 20.987581253051758,loss wh : 5.503872871398926,loss obj : 50.966941833496094，loss cls : 4.985606670379639,loss l2 : 18.50528907775879,lr : 0.001\n",
            "Epoch 10/50:  44%|▍| 264/606 [00:54<01:06,  5.12im2021-03-26 17:21:57,385 train.py[line:353] INFO: Train step_2860: loss : 106.90965270996094,loss xy : 25.218448638916016,loss wh : 1.637830376625061,loss obj : 62.82437515258789，loss cls : 17.228994369506836,loss l2 : 18.234758377075195,lr : 0.001\n",
            "Epoch 10/50:  50%|▌| 304/606 [01:02<00:58,  5.15im2021-03-26 17:22:05,214 train.py[line:353] INFO: Train step_2880: loss : 52.81310272216797,loss xy : 14.226895332336426,loss wh : 0.4352719485759735,loss obj : 36.715179443359375，loss cls : 1.4357579946517944,loss l2 : 4.336742401123047,lr : 0.001\n",
            "Epoch 10/50:  57%|▌| 344/606 [01:10<00:51,  5.09im2021-03-26 17:22:13,078 train.py[line:353] INFO: Train step_2900: loss : 67.9890365600586,loss xy : 14.67278003692627,loss wh : 0.9510788917541504,loss obj : 48.078250885009766，loss cls : 4.286929130554199,loss l2 : 8.079263687133789,lr : 0.001\n",
            "Epoch 10/50:  63%|▋| 384/606 [01:18<00:43,  5.08im2021-03-26 17:22:20,966 train.py[line:353] INFO: Train step_2920: loss : 65.22128295898438,loss xy : 11.051750183105469,loss wh : 1.1077682971954346,loss obj : 40.790245056152344，loss cls : 12.271516799926758,loss l2 : 10.670660018920898,lr : 0.001\n",
            "Epoch 10/50:  70%|▋| 424/606 [01:26<00:35,  5.12im2021-03-26 17:22:28,806 train.py[line:353] INFO: Train step_2940: loss : 46.65403747558594,loss xy : 8.088907241821289,loss wh : 0.43886297941207886,loss obj : 36.46738052368164，loss cls : 1.6588842868804932,loss l2 : 5.148348331451416,lr : 0.001\n",
            "Epoch 10/50:  77%|▊| 464/606 [01:33<00:27,  5.09im2021-03-26 17:22:36,629 train.py[line:353] INFO: Train step_2960: loss : 37.311187744140625,loss xy : 4.6986799240112305,loss wh : 0.02817581035196781,loss obj : 30.663888931274414，loss cls : 1.920442819595337,loss l2 : 2.253535509109497,lr : 0.001\n",
            "Epoch 10/50:  83%|▊| 504/606 [01:41<00:19,  5.16im2021-03-26 17:22:44,381 train.py[line:353] INFO: Train step_2980: loss : 63.32622528076172,loss xy : 18.25432586669922,loss wh : 2.298719882965088,loss obj : 40.94822311401367，loss cls : 1.8249588012695312,loss l2 : 10.008878707885742,lr : 0.001\n",
            "Epoch 10/50:  90%|▉| 544/606 [01:49<00:12,  5.13im2021-03-26 17:22:52,250 train.py[line:353] INFO: Train step_3000: loss : 70.00468444824219,loss xy : 16.338491439819336,loss wh : 0.664151668548584,loss obj : 40.400360107421875，loss cls : 12.601683616638184,loss l2 : 10.934076309204102,lr : 0.001\n",
            "Epoch 10/50:  96%|▉| 584/606 [01:57<00:04,  5.22im2021-03-26 17:23:00,083 train.py[line:353] INFO: Train step_3020: loss : 38.58373260498047,loss xy : 6.886260032653809,loss wh : 0.3770756125450134,loss obj : 29.253326416015625，loss cls : 2.0670719146728516,loss l2 : 3.4291272163391113,lr : 0.001\n",
            "Epoch 10/50: 100%|█| 606/606 [02:01<00:00,  5.25im2021-03-26 17:23:04,443 train.py[line:364] INFO: Checkpoint 10 saved !\n",
            "Epoch 10/50: 100%|█| 606/606 [02:02<00:00,  4.96im\n",
            "Epoch 11/50:   3%| | 18/606 [00:05<02:35,  3.78img2021-03-26 17:23:10,816 train.py[line:353] INFO: Train step_3040: loss : 81.07899475097656,loss xy : 23.582353591918945,loss wh : 1.6342934370040894,loss obj : 52.85237121582031，loss cls : 3.009977340698242,loss l2 : 11.30269718170166,lr : 0.001\n",
            "Epoch 11/50:  10%| | 58/606 [00:13<01:47,  5.10img2021-03-26 17:23:18,708 train.py[line:353] INFO: Train step_3060: loss : 60.06842041015625,loss xy : 16.768714904785156,loss wh : 2.8837766647338867,loss obj : 37.17390823364258，loss cls : 3.242018222808838,loss l2 : 10.045379638671875,lr : 0.001\n",
            "Epoch 11/50:  16%|▏| 98/606 [00:21<01:40,  5.07img2021-03-26 17:23:26,638 train.py[line:353] INFO: Train step_3080: loss : 55.08273696899414,loss xy : 10.000816345214844,loss wh : 0.9127366542816162,loss obj : 35.43299865722656，loss cls : 8.736186981201172,loss l2 : 7.5314621925354,lr : 0.001\n",
            "Epoch 11/50:  23%|▏| 138/606 [00:29<01:31,  5.10im2021-03-26 17:23:34,555 train.py[line:353] INFO: Train step_3100: loss : 41.98966979980469,loss xy : 6.259760856628418,loss wh : 0.20601068437099457,loss obj : 31.44715118408203，loss cls : 4.076746463775635,loss l2 : 4.084870338439941,lr : 0.001\n",
            "Epoch 11/50:  29%|▎| 178/606 [00:37<01:24,  5.07im2021-03-26 17:23:42,429 train.py[line:353] INFO: Train step_3120: loss : 35.072593688964844,loss xy : 5.466615200042725,loss wh : 0.428345263004303,loss obj : 27.15841293334961，loss cls : 2.0192172527313232,loss l2 : 3.2289254665374756,lr : 0.001\n",
            "Epoch 11/50:  36%|▎| 218/606 [00:45<01:16,  5.07im2021-03-26 17:23:50,304 train.py[line:353] INFO: Train step_3140: loss : 49.725093841552734,loss xy : 9.797755241394043,loss wh : 0.7812802791595459,loss obj : 33.87031173706055，loss cls : 5.275744438171387,loss l2 : 6.408229351043701,lr : 0.001\n",
            "Epoch 11/50:  43%|▍| 258/606 [00:53<01:09,  5.04im2021-03-26 17:23:58,221 train.py[line:353] INFO: Train step_3160: loss : 41.0296745300293,loss xy : 9.448885917663574,loss wh : 0.1764298379421234,loss obj : 28.227462768554688，loss cls : 3.176894426345825,loss l2 : 4.23563814163208,lr : 0.001\n",
            "Epoch 11/50:  49%|▍| 298/606 [01:01<01:00,  5.11im2021-03-26 17:24:06,037 train.py[line:353] INFO: Train step_3180: loss : 65.28010559082031,loss xy : 16.125444412231445,loss wh : 0.7773724794387817,loss obj : 41.703922271728516，loss cls : 6.673366069793701,loss l2 : 8.762043952941895,lr : 0.001\n",
            "Epoch 11/50:  56%|▌| 338/606 [01:09<00:52,  5.08im2021-03-26 17:24:13,909 train.py[line:353] INFO: Train step_3200: loss : 45.208526611328125,loss xy : 12.257238388061523,loss wh : 0.5426011085510254,loss obj : 31.40266990661621，loss cls : 1.0060186386108398,loss l2 : 5.4666643142700195,lr : 0.001\n",
            "Epoch 11/50:  62%|▌| 378/606 [01:16<00:44,  5.11im2021-03-26 17:24:21,784 train.py[line:353] INFO: Train step_3220: loss : 85.86566162109375,loss xy : 27.893627166748047,loss wh : 2.392179012298584,loss obj : 47.99886703491211，loss cls : 7.580988883972168,loss l2 : 14.666219711303711,lr : 0.001\n",
            "Epoch 11/50:  69%|▋| 418/606 [01:24<00:36,  5.09im2021-03-26 17:24:29,671 train.py[line:353] INFO: Train step_3240: loss : 70.56484985351562,loss xy : 20.474695205688477,loss wh : 1.7520620822906494,loss obj : 40.253318786621094，loss cls : 8.084775924682617,loss l2 : 11.534911155700684,lr : 0.001\n",
            "Epoch 11/50:  76%|▊| 458/606 [01:32<00:29,  5.05im2021-03-26 17:24:37,516 train.py[line:353] INFO: Train step_3260: loss : 48.16264724731445,loss xy : 17.06406021118164,loss wh : 0.9542762041091919,loss obj : 29.301456451416016，loss cls : 0.8428517580032349,loss l2 : 5.160218238830566,lr : 0.001\n",
            "Epoch 11/50:  82%|▊| 498/606 [01:40<00:21,  5.11im2021-03-26 17:24:45,366 train.py[line:353] INFO: Train step_3280: loss : 45.55344009399414,loss xy : 11.190853118896484,loss wh : 0.49162209033966064,loss obj : 32.423004150390625，loss cls : 1.447960615158081,loss l2 : 5.286774635314941,lr : 0.001\n",
            "Epoch 11/50:  89%|▉| 538/606 [01:48<00:13,  5.10im2021-03-26 17:24:53,232 train.py[line:353] INFO: Train step_3300: loss : 85.77711486816406,loss xy : 24.850082397460938,loss wh : 5.490532398223877,loss obj : 44.315956115722656，loss cls : 11.120542526245117,loss l2 : 20.677946090698242,lr : 0.001\n",
            "Epoch 11/50:  95%|▉| 578/606 [01:56<00:05,  5.18im2021-03-26 17:25:01,043 train.py[line:353] INFO: Train step_3320: loss : 45.19414138793945,loss xy : 12.377429962158203,loss wh : 0.20745334029197693,loss obj : 26.081329345703125，loss cls : 6.527926445007324,loss l2 : 5.102115154266357,lr : 0.001\n",
            "Epoch 11/50: 100%|█| 606/606 [02:01<00:00,  5.25im2021-03-26 17:25:06,533 train.py[line:364] INFO: Checkpoint 11 saved !\n",
            "Epoch 11/50: 100%|█| 606/606 [02:02<00:00,  4.96im\n",
            "Epoch 12/50:   2%| | 12/606 [00:04<03:57,  2.50img2021-03-26 17:25:11,705 train.py[line:353] INFO: Train step_3340: loss : 40.885868072509766,loss xy : 7.570919990539551,loss wh : 0.8257052898406982,loss obj : 28.4325008392334，loss cls : 4.056742191314697,loss l2 : 5.885481834411621,lr : 0.001\n",
            "Epoch 12/50:   9%| | 52/606 [00:12<01:49,  5.06img2021-03-26 17:25:19,540 train.py[line:353] INFO: Train step_3360: loss : 34.68260192871094,loss xy : 7.196125030517578,loss wh : 0.5700069665908813,loss obj : 26.576051712036133，loss cls : 0.34042152762413025,loss l2 : 4.086985111236572,lr : 0.001\n",
            "Epoch 12/50:  15%|▏| 92/606 [00:20<01:41,  5.05img2021-03-26 17:25:27,437 train.py[line:353] INFO: Train step_3380: loss : 74.60945892333984,loss xy : 20.31627082824707,loss wh : 0.7018444538116455,loss obj : 42.62250518798828，loss cls : 10.968839645385742,loss l2 : 11.611672401428223,lr : 0.001\n",
            "Epoch 12/50:  22%|▏| 132/606 [00:28<01:33,  5.08im2021-03-26 17:25:35,352 train.py[line:353] INFO: Train step_3400: loss : 89.67012786865234,loss xy : 32.636253356933594,loss wh : 3.0373895168304443,loss obj : 49.882530212402344，loss cls : 4.113959789276123,loss l2 : 17.345970153808594,lr : 0.001\n",
            "Epoch 12/50:  28%|▎| 172/606 [00:36<01:24,  5.12im2021-03-26 17:25:43,216 train.py[line:353] INFO: Train step_3420: loss : 105.37923431396484,loss xy : 29.598711013793945,loss wh : 3.169966697692871,loss obj : 58.512535095214844，loss cls : 14.098021507263184,loss l2 : 19.63677406311035,lr : 0.001\n",
            "Epoch 12/50:  35%|▎| 212/606 [00:44<01:17,  5.11im2021-03-26 17:25:51,039 train.py[line:353] INFO: Train step_3440: loss : 34.26692581176758,loss xy : 6.034380912780762,loss wh : 0.15094693005084991,loss obj : 24.02245330810547，loss cls : 4.059144973754883,loss l2 : 3.289090633392334,lr : 0.001\n",
            "Epoch 12/50:  42%|▍| 252/606 [00:51<01:08,  5.14im2021-03-26 17:25:58,870 train.py[line:353] INFO: Train step_3460: loss : 96.58067321777344,loss xy : 31.002798080444336,loss wh : 2.327561855316162,loss obj : 53.63330078125，loss cls : 9.617012023925781,loss l2 : 17.106327056884766,lr : 0.001\n",
            "Epoch 12/50:  48%|▍| 292/606 [00:59<01:01,  5.11im2021-03-26 17:26:06,727 train.py[line:353] INFO: Train step_3480: loss : 44.513893127441406,loss xy : 12.091178894042969,loss wh : 0.8635879755020142,loss obj : 26.728761672973633，loss cls : 4.83036470413208,loss l2 : 6.380675792694092,lr : 0.001\n",
            "Epoch 12/50:  55%|▌| 332/606 [01:07<00:53,  5.14im2021-03-26 17:26:14,546 train.py[line:353] INFO: Train step_3500: loss : 54.4110107421875,loss xy : 15.507894515991211,loss wh : 1.2933965921401978,loss obj : 32.507476806640625，loss cls : 5.102241516113281,loss l2 : 8.581153869628906,lr : 0.001\n",
            "Epoch 12/50:  61%|▌| 372/606 [01:15<00:46,  5.08im2021-03-26 17:26:22,342 train.py[line:353] INFO: Train step_3520: loss : 40.882991790771484,loss xy : 9.142683982849121,loss wh : 0.30527934432029724,loss obj : 23.844873428344727，loss cls : 7.590151786804199,loss l2 : 5.446946620941162,lr : 0.001\n",
            "Epoch 12/50:  68%|▋| 412/606 [01:23<00:38,  5.09im2021-03-26 17:26:30,184 train.py[line:353] INFO: Train step_3540: loss : 45.67326736450195,loss xy : 11.130969047546387,loss wh : 1.4280840158462524,loss obj : 29.759796142578125，loss cls : 3.354419231414795,loss l2 : 7.384031295776367,lr : 0.001\n",
            "Epoch 12/50:  75%|▋| 452/606 [01:31<00:30,  5.08im2021-03-26 17:26:38,066 train.py[line:353] INFO: Train step_3560: loss : 50.34571075439453,loss xy : 12.259592056274414,loss wh : 0.6762365102767944,loss obj : 28.227689743041992，loss cls : 9.182191848754883,loss l2 : 7.586050033569336,lr : 0.001\n",
            "Epoch 12/50:  81%|▊| 492/606 [01:39<00:22,  5.12im2021-03-26 17:26:45,942 train.py[line:353] INFO: Train step_3580: loss : 25.455957412719727,loss xy : 6.6611151695251465,loss wh : 0.23527038097381592,loss obj : 18.178983688354492，loss cls : 0.3805885910987854,loss l2 : 2.0351126194000244,lr : 0.001\n",
            "Epoch 12/50:  88%|▉| 532/606 [01:46<00:14,  5.07im2021-03-26 17:26:53,846 train.py[line:353] INFO: Train step_3600: loss : 97.39302825927734,loss xy : 37.14535903930664,loss wh : 2.2473433017730713,loss obj : 49.71086883544922，loss cls : 8.289453506469727,loss l2 : 18.497241973876953,lr : 0.001\n",
            "Epoch 12/50:  94%|▉| 572/606 [01:54<00:06,  5.11im2021-03-26 17:27:01,699 train.py[line:353] INFO: Train step_3620: loss : 26.489572525024414,loss xy : 6.971311092376709,loss wh : 0.15719997882843018,loss obj : 18.97745132446289，loss cls : 0.38361018896102905,loss l2 : 2.058443307876587,lr : 0.001\n",
            "Epoch 12/50: 100%|█| 606/606 [02:01<00:00,  5.25im2021-03-26 17:27:08,352 train.py[line:364] INFO: Checkpoint 12 saved !\n",
            "Epoch 12/50: 100%|█| 606/606 [02:01<00:00,  4.97im\n",
            "Epoch 13/50:   1%| | 6/606 [00:03<07:44,  1.29img/2021-03-26 17:27:12,294 train.py[line:353] INFO: Train step_3640: loss : 31.184001922607422,loss xy : 8.35755443572998,loss wh : 0.8934409618377686,loss obj : 19.767059326171875，loss cls : 2.1659464836120605,loss l2 : 3.601712226867676,lr : 0.001\n",
            "Epoch 13/50:   8%| | 46/606 [00:11<01:51,  5.02img2021-03-26 17:27:20,156 train.py[line:353] INFO: Train step_3660: loss : 34.07362365722656,loss xy : 7.784373760223389,loss wh : 0.33643898367881775,loss obj : 23.96345329284668，loss cls : 1.9893556833267212,loss l2 : 4.693638801574707,lr : 0.001\n",
            "Epoch 13/50:  14%|▏| 86/606 [00:19<01:40,  5.16img2021-03-26 17:27:27,996 train.py[line:353] INFO: Train step_3680: loss : 32.37141418457031,loss xy : 7.3709516525268555,loss wh : 0.9246096611022949,loss obj : 22.553783416748047，loss cls : 1.5220688581466675,loss l2 : 5.597579479217529,lr : 0.001\n",
            "Epoch 13/50:  21%|▏| 126/606 [00:27<01:34,  5.08im2021-03-26 17:27:35,842 train.py[line:353] INFO: Train step_3700: loss : 44.55488586425781,loss xy : 9.561124801635742,loss wh : 3.073866128921509,loss obj : 28.89345932006836，loss cls : 3.0264360904693604,loss l2 : 10.38022518157959,lr : 0.001\n",
            "Epoch 13/50:  27%|▎| 166/606 [00:34<01:26,  5.08im2021-03-26 17:27:43,654 train.py[line:353] INFO: Train step_3720: loss : 26.426307678222656,loss xy : 4.293384552001953,loss wh : 0.4254568815231323,loss obj : 21.181428909301758，loss cls : 0.526036262512207,loss l2 : 2.9857864379882812,lr : 0.001\n",
            "Epoch 13/50:  34%|▎| 206/606 [00:42<01:18,  5.09im2021-03-26 17:27:51,484 train.py[line:353] INFO: Train step_3740: loss : 32.50507354736328,loss xy : 9.744039535522461,loss wh : 0.20736707746982574,loss obj : 17.731473922729492，loss cls : 4.822194576263428,loss l2 : 3.733046531677246,lr : 0.001\n",
            "Epoch 13/50:  41%|▍| 246/606 [00:50<01:10,  5.12im2021-03-26 17:27:59,290 train.py[line:353] INFO: Train step_3760: loss : 33.377769470214844,loss xy : 6.648161888122559,loss wh : 0.7606124877929688,loss obj : 19.85515022277832，loss cls : 6.11384391784668,loss l2 : 5.418351650238037,lr : 0.001\n",
            "Epoch 13/50:  47%|▍| 286/606 [00:58<01:02,  5.12im2021-03-26 17:28:07,119 train.py[line:353] INFO: Train step_3780: loss : 66.1767807006836,loss xy : 11.957786560058594,loss wh : 4.625507354736328,loss obj : 34.758766174316406，loss cls : 14.834722518920898,loss l2 : 19.394634246826172,lr : 0.001\n",
            "Epoch 13/50:  54%|▌| 326/606 [01:06<00:54,  5.11im2021-03-26 17:28:14,994 train.py[line:353] INFO: Train step_3800: loss : 51.34134292602539,loss xy : 20.267282485961914,loss wh : 0.7793513536453247,loss obj : 28.94283676147461，loss cls : 1.3518706560134888,loss l2 : 7.176473617553711,lr : 0.001\n",
            "Epoch 13/50:  60%|▌| 366/606 [01:14<00:46,  5.14im2021-03-26 17:28:22,821 train.py[line:353] INFO: Train step_3820: loss : 43.417537689208984,loss xy : 16.888851165771484,loss wh : 1.4364691972732544,loss obj : 24.39850616455078，loss cls : 0.6937100291252136,loss l2 : 7.103778839111328,lr : 0.001\n",
            "Epoch 13/50:  67%|▋| 406/606 [01:21<00:39,  5.06im2021-03-26 17:28:30,678 train.py[line:353] INFO: Train step_3840: loss : 21.146865844726562,loss xy : 4.635535717010498,loss wh : 0.1569812148809433,loss obj : 16.233816146850586，loss cls : 0.1205320805311203,loss l2 : 1.873864769935608,lr : 0.001\n",
            "Epoch 13/50:  74%|▋| 446/606 [01:29<00:31,  5.11im2021-03-26 17:28:38,548 train.py[line:353] INFO: Train step_3860: loss : 31.70832061767578,loss xy : 12.452327728271484,loss wh : 0.24882060289382935,loss obj : 15.599806785583496，loss cls : 3.407365322113037,loss l2 : 2.6007938385009766,lr : 0.001\n",
            "Epoch 13/50:  80%|▊| 486/606 [01:37<00:23,  5.11im2021-03-26 17:28:46,421 train.py[line:353] INFO: Train step_3880: loss : 30.242156982421875,loss xy : 8.077329635620117,loss wh : 0.4953954517841339,loss obj : 20.519746780395508，loss cls : 1.1496857404708862,loss l2 : 3.758481025695801,lr : 0.001\n",
            "Epoch 13/50:  87%|▊| 526/606 [01:45<00:15,  5.05im2021-03-26 17:28:54,277 train.py[line:353] INFO: Train step_3900: loss : 21.347145080566406,loss xy : 4.632477283477783,loss wh : 0.05296090245246887,loss obj : 16.388904571533203，loss cls : 0.2728029489517212,loss l2 : 1.240349531173706,lr : 0.001\n",
            "Epoch 13/50:  93%|▉| 566/606 [01:53<00:07,  5.19im2021-03-26 17:29:02,114 train.py[line:353] INFO: Train step_3920: loss : 35.12629699707031,loss xy : 6.506141662597656,loss wh : 0.30881166458129883,loss obj : 19.023845672607422，loss cls : 9.287500381469727,loss l2 : 6.0999579429626465,lr : 0.001\n",
            "Epoch 13/50: 100%|█| 606/606 [02:01<00:00,  5.24im2021-03-26 17:29:09,934 train.py[line:364] INFO: Checkpoint 13 saved !\n",
            "Epoch 13/50: 100%|█| 606/606 [02:01<00:00,  4.98im\n",
            "Epoch 14/50:   0%|       | 0/606 [00:00<?, ?img/s]2021-03-26 17:29:12,757 train.py[line:353] INFO: Train step_3940: loss : 45.874732971191406,loss xy : 14.178239822387695,loss wh : 1.2266210317611694,loss obj : 24.45469856262207，loss cls : 6.01517391204834,loss l2 : 7.5547003746032715,lr : 0.001\n",
            "Epoch 14/50:   7%| | 40/606 [00:10<01:52,  5.04img2021-03-26 17:29:20,745 train.py[line:353] INFO: Train step_3960: loss : 27.46383285522461,loss xy : 6.043521881103516,loss wh : 0.2933691740036011,loss obj : 15.665730476379395，loss cls : 5.461211681365967,loss l2 : 3.3855810165405273,lr : 0.001\n",
            "Epoch 14/50:  13%|▏| 80/606 [00:18<01:43,  5.09img2021-03-26 17:29:28,687 train.py[line:353] INFO: Train step_3980: loss : 69.24691772460938,loss xy : 16.736602783203125,loss wh : 2.6812732219696045,loss obj : 37.638763427734375，loss cls : 12.190279006958008,loss l2 : 13.310508728027344,lr : 0.001\n",
            "Epoch 14/50:  20%|▏| 120/606 [00:26<01:36,  5.06im2021-03-26 17:29:36,594 train.py[line:353] INFO: Train step_4000: loss : 38.31366729736328,loss xy : 9.815824508666992,loss wh : 4.242318153381348,loss obj : 22.265132904052734，loss cls : 1.9903907775878906,loss l2 : 11.487937927246094,lr : 0.001\n",
            "Epoch 14/50:  26%|▎| 160/606 [00:34<01:28,  5.07im2021-03-26 17:29:44,489 train.py[line:353] INFO: Train step_4020: loss : 36.83265686035156,loss xy : 8.974769592285156,loss wh : 0.32618486881256104,loss obj : 26.927608489990234，loss cls : 0.6040956377983093,loss l2 : 6.342174530029297,lr : 0.001\n",
            "Epoch 14/50:  33%|▎| 200/606 [00:42<01:19,  5.13im2021-03-26 17:29:52,377 train.py[line:353] INFO: Train step_4040: loss : 32.52811050415039,loss xy : 11.452999114990234,loss wh : 0.4432925581932068,loss obj : 17.788921356201172，loss cls : 2.842895746231079,loss l2 : 4.482973575592041,lr : 0.001\n",
            "Epoch 14/50:  40%|▍| 240/606 [00:49<01:11,  5.10im2021-03-26 17:30:00,239 train.py[line:353] INFO: Train step_4060: loss : 20.49574089050293,loss xy : 5.269986152648926,loss wh : 0.20052985846996307,loss obj : 13.525535583496094，loss cls : 1.4996895790100098,loss l2 : 1.8829243183135986,lr : 0.001\n",
            "Epoch 14/50:  46%|▍| 280/606 [00:57<01:03,  5.12im2021-03-26 17:30:08,099 train.py[line:353] INFO: Train step_4080: loss : 27.636770248413086,loss xy : 5.407647609710693,loss wh : 0.6803946495056152,loss obj : 19.186464309692383，loss cls : 2.36226487159729,loss l2 : 4.581324100494385,lr : 0.001\n",
            "Epoch 14/50:  53%|▌| 320/606 [01:05<00:55,  5.12im2021-03-26 17:30:15,912 train.py[line:353] INFO: Train step_4100: loss : 22.310415267944336,loss xy : 6.669388771057129,loss wh : 0.12378013879060745,loss obj : 13.162126541137695，loss cls : 2.3551201820373535,loss l2 : 1.8438799381256104,lr : 0.001\n",
            "Epoch 14/50:  59%|▌| 360/606 [01:13<00:48,  5.11im2021-03-26 17:30:23,747 train.py[line:353] INFO: Train step_4120: loss : 35.95918655395508,loss xy : 7.201362133026123,loss wh : 1.4985065460205078,loss obj : 20.35887336730957，loss cls : 6.900444030761719,loss l2 : 8.528022766113281,lr : 0.001\n",
            "Epoch 14/50:  66%|▋| 400/606 [01:21<00:40,  5.13im2021-03-26 17:30:31,646 train.py[line:353] INFO: Train step_4140: loss : 94.64798736572266,loss xy : 25.040569305419922,loss wh : 19.572662353515625,loss obj : 33.500282287597656，loss cls : 16.53447914123535,loss l2 : 49.9196662902832,lr : 0.001\n",
            "Epoch 14/50:  73%|▋| 440/606 [01:29<00:32,  5.14im2021-03-26 17:30:39,435 train.py[line:353] INFO: Train step_4160: loss : 24.70376968383789,loss xy : 4.557631969451904,loss wh : 0.08828209340572357,loss obj : 15.228981971740723，loss cls : 4.8288726806640625,loss l2 : 3.533738136291504,lr : 0.001\n",
            "Epoch 14/50:  79%|▊| 480/606 [01:36<00:24,  5.10im2021-03-26 17:30:47,273 train.py[line:353] INFO: Train step_4180: loss : 15.948555946350098,loss xy : 2.31758189201355,loss wh : 0.13322018086910248,loss obj : 11.924869537353516，loss cls : 1.5728846788406372,loss l2 : 1.4963299036026,lr : 0.001\n",
            "Epoch 14/50:  86%|▊| 520/606 [01:44<00:16,  5.14im2021-03-26 17:30:55,100 train.py[line:353] INFO: Train step_4200: loss : 66.09324645996094,loss xy : 14.236433982849121,loss wh : 1.471484661102295,loss obj : 31.905357360839844，loss cls : 18.47997283935547,loss l2 : 15.165122985839844,lr : 0.001\n",
            "Epoch 14/50:  92%|▉| 560/606 [01:52<00:09,  5.06im2021-03-26 17:31:03,008 train.py[line:353] INFO: Train step_4220: loss : 29.41595458984375,loss xy : 9.22263240814209,loss wh : 1.3756074905395508,loss obj : 17.7074031829834，loss cls : 1.1103124618530273,loss l2 : 5.486344337463379,lr : 0.001\n",
            "Epoch 14/50:  99%|▉| 600/606 [02:00<00:01,  5.22im2021-03-26 17:31:10,729 train.py[line:353] INFO: Train step_4240: loss : 29.765962600708008,loss xy : 8.890179634094238,loss wh : 0.9470517039299011,loss obj : 17.50808334350586，loss cls : 2.4206478595733643,loss l2 : 4.683798313140869,lr : 0.001\n",
            "Epoch 14/50: 100%|█| 606/606 [02:01<00:00,  5.21im2021-03-26 17:31:12,041 train.py[line:364] INFO: Checkpoint 14 saved !\n",
            "Epoch 14/50: 100%|█| 606/606 [02:02<00:00,  4.96im\n",
            "Epoch 15/50:   6%| | 34/606 [00:08<01:55,  4.97img2021-03-26 17:31:21,374 train.py[line:353] INFO: Train step_4260: loss : 13.620352745056152,loss xy : 3.3727784156799316,loss wh : 0.06515693664550781,loss obj : 10.096391677856445，loss cls : 0.08602616935968399,loss l2 : 0.39788633584976196,lr : 0.001\n",
            "Epoch 15/50:  12%| | 74/606 [00:16<01:45,  5.05img2021-03-26 17:31:29,236 train.py[line:353] INFO: Train step_4280: loss : 21.621095657348633,loss xy : 3.9114489555358887,loss wh : 0.1628379225730896,loss obj : 16.78241729736328，loss cls : 0.7643917798995972,loss l2 : 2.2629644870758057,lr : 0.001\n",
            "Epoch 15/50:  19%|▏| 114/606 [00:24<01:36,  5.08im2021-03-26 17:31:37,186 train.py[line:353] INFO: Train step_4300: loss : 93.78035736083984,loss xy : 38.135501861572266,loss wh : 7.516085624694824,loss obj : 38.5106315612793，loss cls : 9.618138313293457,loss l2 : 25.266321182250977,lr : 0.001\n",
            "Epoch 15/50:  25%|▎| 154/606 [00:32<01:28,  5.11im2021-03-26 17:31:45,066 train.py[line:353] INFO: Train step_4320: loss : 25.88671875,loss xy : 6.445252895355225,loss wh : 0.2666924297809601,loss obj : 15.65631103515625，loss cls : 3.5184621810913086,loss l2 : 3.8133201599121094,lr : 0.001\n",
            "Epoch 15/50:  32%|▎| 194/606 [00:40<01:21,  5.07im2021-03-26 17:31:52,916 train.py[line:353] INFO: Train step_4340: loss : 25.288965225219727,loss xy : 8.863496780395508,loss wh : 0.5059075951576233,loss obj : 15.134838104248047，loss cls : 0.7847241163253784,loss l2 : 3.235816478729248,lr : 0.001\n",
            "Epoch 15/50:  39%|▍| 234/606 [00:48<01:13,  5.07im2021-03-26 17:32:00,789 train.py[line:353] INFO: Train step_4360: loss : 17.038259506225586,loss xy : 4.344846725463867,loss wh : 0.17860153317451477,loss obj : 11.698997497558594，loss cls : 0.8158131837844849,loss l2 : 1.5473250150680542,lr : 0.001\n",
            "Epoch 15/50:  45%|▍| 274/606 [00:56<01:04,  5.12im2021-03-26 17:32:08,629 train.py[line:353] INFO: Train step_4380: loss : 43.774349212646484,loss xy : 20.156883239746094,loss wh : 1.1071211099624634,loss obj : 21.629680633544922，loss cls : 0.8806656002998352,loss l2 : 7.709968566894531,lr : 0.001\n",
            "Epoch 15/50:  52%|▌| 314/606 [01:04<00:57,  5.10im2021-03-26 17:32:16,450 train.py[line:353] INFO: Train step_4400: loss : 34.59382629394531,loss xy : 13.023093223571777,loss wh : 0.7682068347930908,loss obj : 19.65599822998047，loss cls : 1.1465319395065308,loss l2 : 5.502045631408691,lr : 0.001\n",
            "Epoch 15/50:  58%|▌| 354/606 [01:11<00:49,  5.09im2021-03-26 17:32:24,371 train.py[line:353] INFO: Train step_4420: loss : 35.116886138916016,loss xy : 12.780155181884766,loss wh : 0.43936866521835327,loss obj : 18.291519165039062，loss cls : 3.6058428287506104,loss l2 : 5.661718845367432,lr : 0.001\n",
            "Epoch 15/50:  65%|▋| 394/606 [01:19<00:41,  5.09im2021-03-26 17:32:32,192 train.py[line:353] INFO: Train step_4440: loss : 44.86366271972656,loss xy : 20.999420166015625,loss wh : 0.723262369632721,loss obj : 20.853622436523438，loss cls : 2.2873592376708984,loss l2 : 5.528990745544434,lr : 0.001\n",
            "Epoch 15/50:  72%|▋| 434/606 [01:27<00:33,  5.10im2021-03-26 17:32:40,039 train.py[line:353] INFO: Train step_4460: loss : 30.598583221435547,loss xy : 10.650842666625977,loss wh : 0.5230804085731506,loss obj : 18.72045135498047，loss cls : 0.7042091488838196,loss l2 : 4.290363788604736,lr : 0.001\n",
            "Epoch 15/50:  78%|▊| 474/606 [01:35<00:26,  5.06im2021-03-26 17:32:47,943 train.py[line:353] INFO: Train step_4480: loss : 33.206329345703125,loss xy : 6.329509735107422,loss wh : 1.0440670251846313,loss obj : 21.592037200927734，loss cls : 4.240714073181152,loss l2 : 6.9143476486206055,lr : 0.001\n",
            "Epoch 15/50:  85%|▊| 514/606 [01:43<00:18,  5.10im2021-03-26 17:32:55,814 train.py[line:353] INFO: Train step_4500: loss : 40.49723434448242,loss xy : 15.658374786376953,loss wh : 0.6184104681015015,loss obj : 22.038999557495117，loss cls : 2.181448459625244,loss l2 : 6.1460723876953125,lr : 0.001\n",
            "Epoch 15/50:  91%|▉| 554/606 [01:51<00:10,  5.05im2021-03-26 17:33:03,696 train.py[line:353] INFO: Train step_4520: loss : 18.023792266845703,loss xy : 5.205460548400879,loss wh : 0.25065794587135315,loss obj : 9.55410099029541，loss cls : 3.0135724544525146,loss l2 : 2.2187936305999756,lr : 0.001\n",
            "Epoch 15/50:  98%|▉| 594/606 [01:58<00:02,  5.25im2021-03-26 17:33:11,406 train.py[line:353] INFO: Train step_4540: loss : 16.836076736450195,loss xy : 4.871260166168213,loss wh : 0.1071433573961258,loss obj : 9.20584487915039，loss cls : 2.6518290042877197,loss l2 : 1.6778775453567505,lr : 0.001\n",
            "Epoch 15/50: 100%|█| 606/606 [02:01<00:00,  5.22im2021-03-26 17:33:13,902 train.py[line:364] INFO: Checkpoint 15 saved !\n",
            "Epoch 15/50: 100%|█| 606/606 [02:01<00:00,  4.97im\n",
            "Epoch 16/50:   5%| | 28/606 [00:07<01:59,  4.85img2021-03-26 17:33:21,955 train.py[line:353] INFO: Train step_4560: loss : 24.169403076171875,loss xy : 8.559274673461914,loss wh : 0.29521530866622925,loss obj : 12.739171981811523，loss cls : 2.5757408142089844,loss l2 : 3.10032320022583,lr : 0.001\n",
            "Epoch 16/50:  11%| | 68/606 [00:15<01:47,  5.01img2021-03-26 17:33:29,854 train.py[line:353] INFO: Train step_4580: loss : 13.107242584228516,loss xy : 2.5456056594848633,loss wh : 0.05451023206114769,loss obj : 9.207779884338379，loss cls : 1.2993472814559937,loss l2 : 1.4339959621429443,lr : 0.001\n",
            "Epoch 16/50:  18%|▏| 108/606 [00:23<01:39,  5.00im2021-03-26 17:33:37,847 train.py[line:353] INFO: Train step_4600: loss : 16.07301902770996,loss xy : 7.950396537780762,loss wh : 0.1648789793252945,loss obj : 7.933474063873291，loss cls : 0.02426820993423462,loss l2 : 0.9177166819572449,lr : 0.001\n",
            "Epoch 16/50:  24%|▏| 148/606 [00:31<01:31,  5.01im2021-03-26 17:33:45,816 train.py[line:353] INFO: Train step_4620: loss : 35.088233947753906,loss xy : 10.264144897460938,loss wh : 0.40646618604660034,loss obj : 22.65041732788086，loss cls : 1.767203450202942,loss l2 : 5.566313743591309,lr : 0.001\n",
            "Epoch 16/50:  31%|▎| 188/606 [00:39<01:21,  5.11im2021-03-26 17:33:53,727 train.py[line:353] INFO: Train step_4640: loss : 29.21131134033203,loss xy : 10.016085624694824,loss wh : 0.19550514221191406,loss obj : 11.065102577209473，loss cls : 7.934617042541504,loss l2 : 4.7693586349487305,lr : 0.001\n",
            "Epoch 16/50:  38%|▍| 228/606 [00:47<01:14,  5.06im2021-03-26 17:34:01,614 train.py[line:353] INFO: Train step_4660: loss : 33.763404846191406,loss xy : 15.201029777526855,loss wh : 0.6424039006233215,loss obj : 12.898046493530273，loss cls : 5.021924018859863,loss l2 : 4.775485038757324,lr : 0.001\n",
            "Epoch 16/50:  44%|▍| 268/606 [00:55<01:07,  5.04im2021-03-26 17:34:09,538 train.py[line:353] INFO: Train step_4680: loss : 34.59539794921875,loss xy : 10.544785499572754,loss wh : 0.8912088871002197,loss obj : 21.11399269104004，loss cls : 2.0454092025756836,loss l2 : 6.519115447998047,lr : 0.001\n",
            "Epoch 16/50:  51%|▌| 308/606 [01:03<00:58,  5.13im2021-03-26 17:34:17,435 train.py[line:353] INFO: Train step_4700: loss : 46.372962951660156,loss xy : 16.695100784301758,loss wh : 0.6534044742584229,loss obj : 24.61001968383789，loss cls : 4.414437770843506,loss l2 : 8.990598678588867,lr : 0.001\n",
            "Epoch 16/50:  57%|▌| 348/606 [01:11<00:50,  5.08im2021-03-26 17:34:25,363 train.py[line:353] INFO: Train step_4720: loss : 16.526287078857422,loss xy : 4.559876441955566,loss wh : 0.3918439447879791,loss obj : 10.900835990905762，loss cls : 0.6737307906150818,loss l2 : 2.0614547729492188,lr : 0.001\n",
            "Epoch 16/50:  64%|▋| 388/606 [01:18<00:42,  5.12im2021-03-26 17:34:33,215 train.py[line:353] INFO: Train step_4740: loss : 28.313270568847656,loss xy : 12.942821502685547,loss wh : 0.35839658975601196,loss obj : 13.975996017456055，loss cls : 1.036056399345398,loss l2 : 3.276170253753662,lr : 0.001\n",
            "Epoch 16/50:  71%|▋| 428/606 [01:26<00:34,  5.09im2021-03-26 17:34:41,096 train.py[line:353] INFO: Train step_4760: loss : 46.7509880065918,loss xy : 21.392475128173828,loss wh : 1.1836045980453491,loss obj : 22.831504821777344，loss cls : 1.3434022665023804,loss l2 : 7.522819995880127,lr : 0.001\n",
            "Epoch 16/50:  77%|▊| 468/606 [01:34<00:27,  5.10im2021-03-26 17:34:49,026 train.py[line:353] INFO: Train step_4780: loss : 33.36054611206055,loss xy : 13.253155708312988,loss wh : 0.5532293915748596,loss obj : 18.029048919677734，loss cls : 1.5251117944717407,loss l2 : 5.363829612731934,lr : 0.001\n",
            "Epoch 16/50:  84%|▊| 508/606 [01:42<00:19,  5.02im2021-03-26 17:34:56,944 train.py[line:353] INFO: Train step_4800: loss : 48.03933334350586,loss xy : 15.341193199157715,loss wh : 0.7648986577987671,loss obj : 30.30891227722168，loss cls : 1.624328374862671,loss l2 : 7.3340373039245605,lr : 0.001\n",
            "Epoch 16/50:  90%|▉| 548/606 [01:50<00:11,  5.09im2021-03-26 17:35:04,802 train.py[line:353] INFO: Train step_4820: loss : 18.576309204101562,loss xy : 9.451498031616211,loss wh : 0.2852890193462372,loss obj : 8.836307525634766，loss cls : 0.0032157087698578835,loss l2 : 1.3795347213745117,lr : 0.001\n",
            "Epoch 16/50:  97%|▉| 588/606 [01:58<00:03,  5.23im2021-03-26 17:35:12,593 train.py[line:353] INFO: Train step_4840: loss : 20.977481842041016,loss xy : 6.313440322875977,loss wh : 0.26820793747901917,loss obj : 9.663825988769531，loss cls : 4.732008934020996,loss l2 : 3.6420395374298096,lr : 0.001\n",
            "Epoch 16/50: 100%|█| 606/606 [02:01<00:00,  5.21im2021-03-26 17:35:16,214 train.py[line:364] INFO: Checkpoint 16 saved !\n",
            "Epoch 16/50: 100%|█| 606/606 [02:02<00:00,  4.95im\n",
            "Epoch 17/50:   4%| | 22/606 [00:06<02:16,  4.28img2021-03-26 17:35:23,460 train.py[line:353] INFO: Train step_4860: loss : 19.053049087524414,loss xy : 8.546863555908203,loss wh : 0.36426323652267456,loss obj : 10.063623428344727，loss cls : 0.07829771190881729,loss l2 : 2.330094337463379,lr : 0.001\n",
            "Epoch 17/50:  10%| | 62/606 [00:14<01:48,  5.02img2021-03-26 17:35:31,376 train.py[line:353] INFO: Train step_4880: loss : 54.213600158691406,loss xy : 19.417747497558594,loss wh : 1.2074050903320312,loss obj : 25.498058319091797，loss cls : 8.090391159057617,loss l2 : 9.278007507324219,lr : 0.001\n",
            "Epoch 17/50:  17%|▏| 102/606 [00:22<01:40,  5.00im2021-03-26 17:35:39,308 train.py[line:353] INFO: Train step_4900: loss : 37.214664459228516,loss xy : 12.047004699707031,loss wh : 1.304259181022644,loss obj : 22.74526596069336，loss cls : 1.1181347370147705,loss l2 : 8.42501449584961,lr : 0.001\n",
            "Epoch 17/50:  23%|▏| 142/606 [00:30<01:32,  5.01im2021-03-26 17:35:47,287 train.py[line:353] INFO: Train step_4920: loss : 20.685274124145508,loss xy : 3.6403017044067383,loss wh : 0.09406878799200058,loss obj : 16.621604919433594，loss cls : 0.32929837703704834,loss l2 : 3.025470733642578,lr : 0.001\n",
            "Epoch 17/50:  30%|▎| 182/606 [00:38<01:24,  5.01im2021-03-26 17:35:55,248 train.py[line:353] INFO: Train step_4940: loss : 16.825645446777344,loss xy : 6.787829875946045,loss wh : 0.5482385158538818,loss obj : 9.28724193572998，loss cls : 0.20233440399169922,loss l2 : 2.25569486618042,lr : 0.001\n",
            "Epoch 17/50:  37%|▎| 222/606 [00:46<01:15,  5.08im2021-03-26 17:36:03,177 train.py[line:353] INFO: Train step_4960: loss : 55.591346740722656,loss xy : 20.009185791015625,loss wh : 3.8907628059387207,loss obj : 29.316511154174805，loss cls : 2.374885320663452,loss l2 : 14.201929092407227,lr : 0.001\n",
            "Epoch 17/50:  43%|▍| 262/606 [00:54<01:09,  4.94im2021-03-26 17:36:11,205 train.py[line:353] INFO: Train step_4980: loss : 72.5323486328125,loss xy : 23.943187713623047,loss wh : 1.8864266872406006,loss obj : 33.90410232543945，loss cls : 12.798632621765137,loss l2 : 13.196816444396973,lr : 0.001\n",
            "Epoch 17/50:  50%|▍| 302/606 [01:02<00:59,  5.07im2021-03-26 17:36:19,147 train.py[line:353] INFO: Train step_5000: loss : 38.51654052734375,loss xy : 11.271678924560547,loss wh : 0.7072951197624207,loss obj : 23.21864128112793，loss cls : 3.31892466545105,loss l2 : 8.224654197692871,lr : 0.001\n",
            "Epoch 17/50:  56%|▌| 342/606 [01:10<00:52,  5.00im2021-03-26 17:36:27,117 train.py[line:353] INFO: Train step_5020: loss : 17.788599014282227,loss xy : 5.717972278594971,loss wh : 0.1533598005771637,loss obj : 9.997011184692383，loss cls : 1.9202549457550049,loss l2 : 2.4890944957733154,lr : 0.001\n",
            "Epoch 17/50:  63%|▋| 382/606 [01:18<00:44,  5.02im2021-03-26 17:36:35,082 train.py[line:353] INFO: Train step_5040: loss : 38.86439514160156,loss xy : 11.596532821655273,loss wh : 1.0361255407333374,loss obj : 17.31532859802246，loss cls : 8.916409492492676,loss l2 : 6.284433841705322,lr : 0.001\n",
            "Epoch 17/50:  70%|▋| 422/606 [01:26<00:35,  5.14im2021-03-26 17:36:42,975 train.py[line:353] INFO: Train step_5060: loss : 24.00749969482422,loss xy : 7.2063703536987305,loss wh : 0.41938626766204834,loss obj : 10.889629364013672，loss cls : 5.49211311340332,loss l2 : 4.777554512023926,lr : 0.001\n",
            "Epoch 17/50:  76%|▊| 462/606 [01:34<00:28,  5.06im2021-03-26 17:36:50,854 train.py[line:353] INFO: Train step_5080: loss : 39.29037094116211,loss xy : 16.250709533691406,loss wh : 0.7712424397468567,loss obj : 18.379932403564453，loss cls : 3.8884849548339844,loss l2 : 5.95021915435791,lr : 0.001\n",
            "Epoch 17/50:  83%|▊| 502/606 [01:42<00:20,  5.18im2021-03-26 17:36:58,690 train.py[line:353] INFO: Train step_5100: loss : 25.27882957458496,loss xy : 9.747696876525879,loss wh : 1.0884208679199219,loss obj : 14.058581352233887，loss cls : 0.3841306269168854,loss l2 : 5.158062934875488,lr : 0.001\n",
            "Epoch 17/50:  89%|▉| 542/606 [01:49<00:12,  5.14im2021-03-26 17:37:06,600 train.py[line:353] INFO: Train step_5120: loss : 60.64508056640625,loss xy : 23.552196502685547,loss wh : 1.1330676078796387,loss obj : 30.96141242980957，loss cls : 4.998405456542969,loss l2 : 11.255318641662598,lr : 0.001\n",
            "Epoch 17/50:  96%|▉| 582/606 [01:57<00:04,  5.15im2021-03-26 17:37:14,494 train.py[line:353] INFO: Train step_5140: loss : 13.967215538024902,loss xy : 6.954461097717285,loss wh : 0.37431854009628296,loss obj : 6.6315836906433105，loss cls : 0.0068519520573318005,loss l2 : 1.0451375246047974,lr : 0.001\n",
            "Epoch 17/50: 100%|█| 606/606 [02:02<00:00,  5.21im2021-03-26 17:37:19,250 train.py[line:364] INFO: Checkpoint 17 saved !\n",
            "Epoch 17/50: 100%|█| 606/606 [02:03<00:00,  4.93im\n",
            "Epoch 18/50:   3%| | 16/606 [00:05<02:48,  3.50img2021-03-26 17:37:25,069 train.py[line:353] INFO: Train step_5160: loss : 17.568418502807617,loss xy : 4.795034408569336,loss wh : 0.2565925121307373,loss obj : 12.269058227539062，loss cls : 0.24773259460926056,loss l2 : 2.8833203315734863,lr : 0.001\n",
            "Epoch 18/50:   9%| | 56/606 [00:13<01:48,  5.09img2021-03-26 17:37:32,896 train.py[line:353] INFO: Train step_5180: loss : 35.33880615234375,loss xy : 13.009716033935547,loss wh : 0.6043527722358704,loss obj : 11.879289627075195，loss cls : 9.845449447631836,loss l2 : 6.693531513214111,lr : 0.001\n",
            "Epoch 18/50:  16%|▏| 96/606 [00:21<01:41,  5.04img2021-03-26 17:37:40,761 train.py[line:353] INFO: Train step_5200: loss : 24.66930389404297,loss xy : 9.966853141784668,loss wh : 0.295519083738327,loss obj : 8.345460891723633，loss cls : 6.061470031738281,loss l2 : 3.793377161026001,lr : 0.001\n",
            "Epoch 18/50:  22%|▏| 136/606 [00:28<01:32,  5.08im2021-03-26 17:37:48,621 train.py[line:353] INFO: Train step_5220: loss : 27.0142822265625,loss xy : 12.35174560546875,loss wh : 0.25998133420944214,loss obj : 11.816174507141113，loss cls : 2.5863795280456543,loss l2 : 3.4743473529815674,lr : 0.001\n",
            "Epoch 18/50:  29%|▎| 176/606 [00:36<01:25,  5.03im2021-03-26 17:37:56,540 train.py[line:353] INFO: Train step_5240: loss : 17.130592346191406,loss xy : 6.318373680114746,loss wh : 0.6039015650749207,loss obj : 9.78311824798584，loss cls : 0.4251992106437683,loss l2 : 3.0320587158203125,lr : 0.001\n",
            "Epoch 18/50:  36%|▎| 216/606 [00:44<01:16,  5.07im2021-03-26 17:38:04,465 train.py[line:353] INFO: Train step_5260: loss : 38.434818267822266,loss xy : 24.56562614440918,loss wh : 0.7567324638366699,loss obj : 12.756495475769043，loss cls : 0.3559628427028656,loss l2 : 4.734358787536621,lr : 0.001\n",
            "Epoch 18/50:  42%|▍| 256/606 [00:52<01:09,  5.07im2021-03-26 17:38:12,417 train.py[line:353] INFO: Train step_5280: loss : 35.7527961730957,loss xy : 18.3009033203125,loss wh : 0.6657736897468567,loss obj : 14.976238250732422，loss cls : 1.8098801374435425,loss l2 : 5.041226387023926,lr : 0.001\n",
            "Epoch 18/50:  49%|▍| 296/606 [01:00<01:01,  5.06im2021-03-26 17:38:20,303 train.py[line:353] INFO: Train step_5300: loss : 9.737659454345703,loss xy : 3.661116600036621,loss wh : 0.2523062825202942,loss obj : 5.606862545013428，loss cls : 0.21737389266490936,loss l2 : 0.8010315299034119,lr : 0.001\n",
            "Epoch 18/50:  55%|▌| 336/606 [01:08<00:53,  5.07im2021-03-26 17:38:28,212 train.py[line:353] INFO: Train step_5320: loss : 19.46833610534668,loss xy : 10.888690948486328,loss wh : 0.16554181277751923,loss obj : 8.351001739501953，loss cls : 0.06310143321752548,loss l2 : 1.6783227920532227,lr : 0.001\n",
            "Epoch 18/50:  62%|▌| 376/606 [01:16<00:45,  5.07im2021-03-26 17:38:36,103 train.py[line:353] INFO: Train step_5340: loss : 17.550537109375,loss xy : 11.051399230957031,loss wh : 0.23971736431121826,loss obj : 6.19923734664917，loss cls : 0.060181763023138046,loss l2 : 1.0284194946289062,lr : 0.001\n",
            "Epoch 18/50:  69%|▋| 416/606 [01:24<00:37,  5.07im2021-03-26 17:38:43,999 train.py[line:353] INFO: Train step_5360: loss : 67.39331817626953,loss xy : 15.663970947265625,loss wh : 0.7734586000442505,loss obj : 35.04403305053711，loss cls : 15.911858558654785,loss l2 : 9.39900016784668,lr : 0.001\n",
            "Epoch 18/50:  75%|▊| 456/606 [01:32<00:29,  5.03im2021-03-26 17:38:51,906 train.py[line:353] INFO: Train step_5380: loss : 20.752304077148438,loss xy : 7.923829555511475,loss wh : 0.15917247533798218,loss obj : 12.57249641418457，loss cls : 0.09680549055337906,loss l2 : 3.1269707679748535,lr : 0.001\n",
            "Epoch 18/50:  82%|▊| 496/606 [01:40<00:21,  5.09im2021-03-26 17:38:59,768 train.py[line:353] INFO: Train step_5400: loss : 38.784976959228516,loss xy : 21.323261260986328,loss wh : 0.837222158908844,loss obj : 15.186797142028809，loss cls : 1.4376952648162842,loss l2 : 5.273557186126709,lr : 0.001\n",
            "Epoch 18/50:  88%|▉| 536/606 [01:47<00:13,  5.05im2021-03-26 17:39:07,611 train.py[line:353] INFO: Train step_5420: loss : 22.05484390258789,loss xy : 12.568231582641602,loss wh : 0.46515023708343506,loss obj : 8.970516204833984，loss cls : 0.050946079194545746,loss l2 : 2.248183250427246,lr : 0.001\n",
            "Epoch 18/50:  95%|▉| 576/606 [01:55<00:05,  5.19im2021-03-26 17:39:15,377 train.py[line:353] INFO: Train step_5440: loss : 32.02111053466797,loss xy : 13.390891075134277,loss wh : 1.8761180639266968,loss obj : 16.047382354736328，loss cls : 0.7067200541496277,loss l2 : 6.730704307556152,lr : 0.001\n",
            "Epoch 18/50: 100%|█| 606/606 [02:01<00:00,  5.24im2021-03-26 17:39:21,284 train.py[line:364] INFO: Checkpoint 18 saved !\n",
            "Epoch 18/50: 100%|█| 606/606 [02:02<00:00,  4.97im\n",
            "Epoch 19/50:   2%| | 10/606 [00:04<04:32,  2.19img2021-03-26 17:39:25,927 train.py[line:353] INFO: Train step_5460: loss : 46.239295959472656,loss xy : 23.799335479736328,loss wh : 1.3489080667495728,loss obj : 17.539262771606445，loss cls : 3.5517871379852295,loss l2 : 7.6697306632995605,lr : 0.001\n",
            "Epoch 19/50:   8%| | 50/606 [00:12<01:51,  5.00img2021-03-26 17:39:33,875 train.py[line:353] INFO: Train step_5480: loss : 24.527297973632812,loss xy : 14.165162086486816,loss wh : 1.0501564741134644,loss obj : 8.756535530090332，loss cls : 0.555443525314331,loss l2 : 3.6110832691192627,lr : 0.001\n",
            "Epoch 19/50:  15%|▏| 90/606 [00:20<01:41,  5.07img2021-03-26 17:39:41,806 train.py[line:353] INFO: Train step_5500: loss : 46.754634857177734,loss xy : 15.988962173461914,loss wh : 0.5682010054588318,loss obj : 29.024166107177734，loss cls : 1.1733052730560303,loss l2 : 6.29605770111084,lr : 0.001\n",
            "Epoch 19/50:  21%|▏| 130/606 [00:28<01:34,  5.05im2021-03-26 17:39:49,698 train.py[line:353] INFO: Train step_5520: loss : 29.299571990966797,loss xy : 11.018486022949219,loss wh : 0.1039743423461914,loss obj : 12.794296264648438，loss cls : 5.382817268371582,loss l2 : 4.277884483337402,lr : 0.001\n",
            "Epoch 19/50:  28%|▎| 170/606 [00:35<01:26,  5.05im2021-03-26 17:39:57,618 train.py[line:353] INFO: Train step_5540: loss : 27.14173698425293,loss xy : 10.886259078979492,loss wh : 1.3449196815490723,loss obj : 12.100381851196289，loss cls : 2.810176372528076,loss l2 : 6.317358493804932,lr : 0.001\n",
            "Epoch 19/50:  35%|▎| 210/606 [00:43<01:17,  5.14im2021-03-26 17:40:05,478 train.py[line:353] INFO: Train step_5560: loss : 20.73775291442871,loss xy : 6.177022933959961,loss wh : 0.1196727380156517,loss obj : 11.67203426361084，loss cls : 2.769021987915039,loss l2 : 3.0670926570892334,lr : 0.001\n",
            "Epoch 19/50:  41%|▍| 250/606 [00:51<01:09,  5.14im2021-03-26 17:40:13,303 train.py[line:353] INFO: Train step_5580: loss : 24.874591827392578,loss xy : 11.7931547164917,loss wh : 0.9094550609588623,loss obj : 10.800457954406738，loss cls : 1.3715256452560425,loss l2 : 4.8076324462890625,lr : 0.001\n",
            "Epoch 19/50:  48%|▍| 290/606 [00:59<01:02,  5.09im2021-03-26 17:40:21,199 train.py[line:353] INFO: Train step_5600: loss : 48.13484191894531,loss xy : 17.632007598876953,loss wh : 0.4168643653392792,loss obj : 22.1634521484375，loss cls : 7.92251443862915,loss l2 : 7.272566318511963,lr : 0.001\n",
            "Epoch 19/50:  54%|▌| 330/606 [01:07<00:54,  5.08im2021-03-26 17:40:29,048 train.py[line:353] INFO: Train step_5620: loss : 16.75772476196289,loss xy : 9.378802299499512,loss wh : 0.48011520504951477,loss obj : 4.703793525695801，loss cls : 2.195013999938965,loss l2 : 2.14732027053833,lr : 0.001\n",
            "Epoch 19/50:  61%|▌| 370/606 [01:15<00:46,  5.05im2021-03-26 17:40:36,870 train.py[line:353] INFO: Train step_5640: loss : 24.093364715576172,loss xy : 13.692179679870605,loss wh : 0.32039958238601685,loss obj : 9.95649528503418，loss cls : 0.124291330575943,loss l2 : 3.1133787631988525,lr : 0.001\n",
            "Epoch 19/50:  68%|▋| 410/606 [01:23<00:38,  5.14im2021-03-26 17:40:44,715 train.py[line:353] INFO: Train step_5660: loss : 9.41560173034668,loss xy : 3.8166186809539795,loss wh : 0.2264031022787094,loss obj : 5.072939395904541，loss cls : 0.29964035749435425,loss l2 : 0.5983705520629883,lr : 0.001\n",
            "Epoch 19/50:  74%|▋| 450/606 [01:30<00:30,  5.06im2021-03-26 17:40:52,567 train.py[line:353] INFO: Train step_5680: loss : 33.2781867980957,loss xy : 18.119247436523438,loss wh : 0.9794908165931702,loss obj : 9.685750007629395，loss cls : 4.493697643280029,loss l2 : 5.574924945831299,lr : 0.001\n",
            "Epoch 19/50:  81%|▊| 490/606 [01:38<00:22,  5.08im2021-03-26 17:41:00,540 train.py[line:353] INFO: Train step_5700: loss : 19.037132263183594,loss xy : 6.876225471496582,loss wh : 0.23435765504837036,loss obj : 9.64197826385498，loss cls : 2.2845699787139893,loss l2 : 3.392573595046997,lr : 0.001\n",
            "Epoch 19/50:  87%|▊| 530/606 [01:46<00:14,  5.08im2021-03-26 17:41:08,431 train.py[line:353] INFO: Train step_5720: loss : 14.541960716247559,loss xy : 6.666698455810547,loss wh : 0.06476633250713348,loss obj : 6.587270736694336，loss cls : 1.223225474357605,loss l2 : 1.6950362920761108,lr : 0.001\n",
            "Epoch 19/50:  94%|▉| 570/606 [01:54<00:07,  5.06im2021-03-26 17:41:16,355 train.py[line:353] INFO: Train step_5740: loss : 51.967674255371094,loss xy : 25.162776947021484,loss wh : 1.9130371809005737,loss obj : 20.267061233520508，loss cls : 4.6248016357421875,loss l2 : 10.358236312866211,lr : 0.001\n",
            "Epoch 19/50: 100%|█| 606/606 [02:01<00:00,  5.23im2021-03-26 17:41:23,393 train.py[line:364] INFO: Checkpoint 19 saved !\n",
            "Epoch 19/50: 100%|█| 606/606 [02:02<00:00,  4.96im\n",
            "Epoch 20/50:   1%| | 4/606 [00:02<09:24,  1.07img/2021-03-26 17:41:26,715 train.py[line:353] INFO: Train step_5760: loss : 40.04210662841797,loss xy : 18.391674041748047,loss wh : 1.0750170946121216,loss obj : 16.854520797729492，loss cls : 3.720895767211914,loss l2 : 6.2472944259643555,lr : 0.001\n",
            "Epoch 20/50:   7%| | 44/606 [00:10<01:51,  5.05img2021-03-26 17:41:34,537 train.py[line:353] INFO: Train step_5780: loss : 6.195467948913574,loss xy : 1.8608472347259521,loss wh : 0.05668998882174492,loss obj : 4.216800212860107，loss cls : 0.06113072857260704,loss l2 : 0.4236110746860504,lr : 0.001\n",
            "Epoch 20/50:  14%|▏| 84/606 [00:18<01:43,  5.06img2021-03-26 17:41:42,418 train.py[line:353] INFO: Train step_5800: loss : 35.136695861816406,loss xy : 15.18579387664795,loss wh : 0.6058915853500366,loss obj : 13.684718132019043，loss cls : 5.660294055938721,loss l2 : 6.047677516937256,lr : 0.001\n",
            "Epoch 20/50:  20%|▏| 124/606 [00:26<01:35,  5.06im2021-03-26 17:41:50,345 train.py[line:353] INFO: Train step_5820: loss : 35.488704681396484,loss xy : 14.446155548095703,loss wh : 0.5780036449432373,loss obj : 13.534476280212402，loss cls : 6.930067539215088,loss l2 : 5.96741247177124,lr : 0.001\n",
            "Epoch 20/50:  27%|▎| 164/606 [00:34<01:27,  5.05im2021-03-26 17:41:58,312 train.py[line:353] INFO: Train step_5840: loss : 40.933013916015625,loss xy : 14.983599662780762,loss wh : 1.2870614528656006,loss obj : 19.108488082885742，loss cls : 5.553861618041992,loss l2 : 8.191181182861328,lr : 0.001\n",
            "Epoch 20/50:  34%|▎| 204/606 [00:42<01:19,  5.09im2021-03-26 17:42:06,191 train.py[line:353] INFO: Train step_5860: loss : 8.908878326416016,loss xy : 3.5141820907592773,loss wh : 0.01846485398709774,loss obj : 5.211507797241211，loss cls : 0.16472291946411133,loss l2 : 0.7209233641624451,lr : 0.001\n",
            "Epoch 20/50:  40%|▍| 244/606 [00:50<01:10,  5.11im2021-03-26 17:42:14,078 train.py[line:353] INFO: Train step_5880: loss : 51.174774169921875,loss xy : 18.25306510925293,loss wh : 0.9552485942840576,loss obj : 20.51499366760254，loss cls : 11.451469421386719,loss l2 : 10.839037895202637,lr : 0.001\n",
            "Epoch 20/50:  47%|▍| 284/606 [00:58<01:03,  5.09im2021-03-26 17:42:21,957 train.py[line:353] INFO: Train step_5900: loss : 32.71247863769531,loss xy : 17.28048324584961,loss wh : 0.3693378269672394,loss obj : 13.467223167419434，loss cls : 1.5954375267028809,loss l2 : 4.723508834838867,lr : 0.001\n",
            "Epoch 20/50:  53%|▌| 324/606 [01:06<00:55,  5.05im2021-03-26 17:42:29,859 train.py[line:353] INFO: Train step_5920: loss : 71.52587890625,loss xy : 17.258502960205078,loss wh : 2.550814151763916,loss obj : 39.63240051269531，loss cls : 12.084162712097168,loss l2 : 13.410163879394531,lr : 0.001\n",
            "Epoch 20/50:  60%|▌| 364/606 [01:13<00:48,  5.04im2021-03-26 17:42:37,795 train.py[line:353] INFO: Train step_5940: loss : 40.36174774169922,loss xy : 20.311120986938477,loss wh : 0.7689879536628723,loss obj : 12.511926651000977，loss cls : 6.769712924957275,loss l2 : 6.132831573486328,lr : 0.001\n",
            "Epoch 20/50:  67%|▋| 404/606 [01:21<00:39,  5.12im2021-03-26 17:42:45,683 train.py[line:353] INFO: Train step_5960: loss : 23.970340728759766,loss xy : 13.502089500427246,loss wh : 0.4570864737033844,loss obj : 9.245848655700684，loss cls : 0.7653166651725769,loss l2 : 3.1201813220977783,lr : 0.001\n",
            "Epoch 20/50:  73%|▋| 444/606 [01:29<00:31,  5.08im2021-03-26 17:42:53,541 train.py[line:353] INFO: Train step_5980: loss : 15.756035804748535,loss xy : 2.797537088394165,loss wh : 0.037081100046634674,loss obj : 12.339935302734375，loss cls : 0.5814825892448425,loss l2 : 2.8717315196990967,lr : 0.001\n",
            "Epoch 20/50:  80%|▊| 484/606 [01:37<00:24,  5.07im2021-03-26 17:43:01,458 train.py[line:353] INFO: Train step_6000: loss : 63.96131896972656,loss xy : 23.83953094482422,loss wh : 1.3263188600540161,loss obj : 26.214521408081055，loss cls : 12.580949783325195,loss l2 : 12.517302513122559,lr : 0.001\n",
            "Epoch 20/50:  86%|▊| 524/606 [01:45<00:16,  5.06im2021-03-26 17:43:09,332 train.py[line:353] INFO: Train step_6020: loss : 19.982009887695312,loss xy : 11.608287811279297,loss wh : 0.3440059423446655,loss obj : 7.837769985198975，loss cls : 0.19194696843624115,loss l2 : 2.37383770942688,lr : 0.001\n",
            "Epoch 20/50:  93%|▉| 564/606 [01:53<00:08,  5.08im2021-03-26 17:43:17,208 train.py[line:353] INFO: Train step_6040: loss : 21.67473602294922,loss xy : 8.780438423156738,loss wh : 0.5481165647506714,loss obj : 11.291345596313477，loss cls : 1.0548360347747803,loss l2 : 3.124919891357422,lr : 0.001\n",
            "Epoch 20/50: 100%|▉| 604/606 [02:01<00:00,  5.26im2021-03-26 17:43:24,856 train.py[line:353] INFO: Train step_6060: loss : 52.84293746948242,loss xy : 30.797985076904297,loss wh : 0.9766404628753662,loss obj : 19.303617477416992，loss cls : 1.7646924257278442,loss l2 : 8.765854835510254,lr : 0.001\n",
            "Epoch 20/50: 100%|█| 606/606 [02:01<00:00,  5.20im2021-03-26 17:43:25,376 train.py[line:364] INFO: Checkpoint 20 saved !\n",
            "Epoch 20/50: 100%|█| 606/606 [02:01<00:00,  4.97im\n",
            "Epoch 21/50:   6%| | 38/606 [00:09<01:53,  4.99img2021-03-26 17:43:35,777 train.py[line:353] INFO: Train step_6080: loss : 23.572467803955078,loss xy : 9.620134353637695,loss wh : 0.434214323759079,loss obj : 10.423351287841797，loss cls : 3.094769239425659,loss l2 : 3.740893840789795,lr : 0.001\n",
            "Epoch 21/50:  13%|▏| 78/606 [00:17<01:45,  5.01img2021-03-26 17:43:43,725 train.py[line:353] INFO: Train step_6100: loss : 20.961475372314453,loss xy : 11.186996459960938,loss wh : 0.34764331579208374,loss obj : 8.888558387756348，loss cls : 0.5382771492004395,loss l2 : 2.445385217666626,lr : 0.001\n",
            "Epoch 21/50:  19%|▏| 118/606 [00:25<01:36,  5.05im2021-03-26 17:43:51,640 train.py[line:353] INFO: Train step_6120: loss : 19.609569549560547,loss xy : 9.951183319091797,loss wh : 0.34961313009262085,loss obj : 9.215995788574219，loss cls : 0.09277674555778503,loss l2 : 2.996823787689209,lr : 0.001\n",
            "Epoch 21/50:  26%|▎| 158/606 [00:33<01:29,  5.02im2021-03-26 17:43:59,679 train.py[line:353] INFO: Train step_6140: loss : 64.91197204589844,loss xy : 27.137845993041992,loss wh : 2.498847723007202,loss obj : 29.560169219970703，loss cls : 5.715106964111328,loss l2 : 13.565815925598145,lr : 0.001\n",
            "Epoch 21/50:  33%|▎| 198/606 [00:41<01:21,  5.04im2021-03-26 17:44:07,650 train.py[line:353] INFO: Train step_6160: loss : 13.130685806274414,loss xy : 7.414121150970459,loss wh : 0.0018637339817360044,loss obj : 4.360029697418213，loss cls : 1.354671835899353,loss l2 : 0.9651177525520325,lr : 0.001\n",
            "Epoch 21/50:  39%|▍| 238/606 [00:49<01:12,  5.08im2021-03-26 17:44:15,563 train.py[line:353] INFO: Train step_6180: loss : 18.854961395263672,loss xy : 8.847577095031738,loss wh : 0.4708159863948822,loss obj : 9.118858337402344，loss cls : 0.4177086353302002,loss l2 : 2.5031168460845947,lr : 0.001\n",
            "Epoch 21/50:  46%|▍| 278/606 [00:57<01:04,  5.10im2021-03-26 17:44:23,386 train.py[line:353] INFO: Train step_6200: loss : 26.481950759887695,loss xy : 15.131734848022461,loss wh : 0.7591777443885803,loss obj : 9.507139205932617，loss cls : 1.0838984251022339,loss l2 : 4.047311305999756,lr : 0.001\n",
            "Epoch 21/50:  52%|▌| 318/606 [01:05<00:56,  5.09im2021-03-26 17:44:31,251 train.py[line:353] INFO: Train step_6220: loss : 34.654762268066406,loss xy : 14.588451385498047,loss wh : 0.906830906867981,loss obj : 18.881404876708984，loss cls : 0.2780746817588806,loss l2 : 6.383305072784424,lr : 0.001\n",
            "Epoch 21/50:  59%|▌| 358/606 [01:13<00:48,  5.07im2021-03-26 17:44:39,159 train.py[line:353] INFO: Train step_6240: loss : 7.988464832305908,loss xy : 3.458179235458374,loss wh : 0.053090982139110565,loss obj : 4.223381519317627，loss cls : 0.25381332635879517,loss l2 : 0.6044002175331116,lr : 0.001\n",
            "Epoch 21/50:  66%|▋| 398/606 [01:21<00:41,  5.06im2021-03-26 17:44:47,053 train.py[line:353] INFO: Train step_6260: loss : 13.73963451385498,loss xy : 5.57688570022583,loss wh : 0.26318633556365967,loss obj : 7.7795209884643555，loss cls : 0.12004146724939346,loss l2 : 2.3836262226104736,lr : 0.001\n",
            "Epoch 21/50:  72%|▋| 438/606 [01:29<00:32,  5.12im2021-03-26 17:44:54,912 train.py[line:353] INFO: Train step_6280: loss : 50.86461639404297,loss xy : 22.294803619384766,loss wh : 8.754327774047852,loss obj : 19.30423355102539，loss cls : 0.5112544894218445,loss l2 : 21.54874038696289,lr : 0.001\n",
            "Epoch 21/50:  79%|▊| 478/606 [01:37<00:25,  5.03im2021-03-26 17:45:02,838 train.py[line:353] INFO: Train step_6300: loss : 22.775785446166992,loss xy : 11.169829368591309,loss wh : 0.40787041187286377,loss obj : 10.949454307556152，loss cls : 0.24863311648368835,loss l2 : 3.4383316040039062,lr : 0.001\n",
            "Epoch 21/50:  85%|▊| 518/606 [01:44<00:17,  5.07im2021-03-26 17:45:10,685 train.py[line:353] INFO: Train step_6320: loss : 39.580810546875,loss xy : 17.57335662841797,loss wh : 1.4461956024169922,loss obj : 17.54531478881836，loss cls : 3.015944480895996,loss l2 : 8.194758415222168,lr : 0.001\n",
            "Epoch 21/50:  92%|▉| 558/606 [01:52<00:09,  5.03im2021-03-26 17:45:18,615 train.py[line:353] INFO: Train step_6340: loss : 23.70654296875,loss xy : 11.84670639038086,loss wh : 0.2901065945625305,loss obj : 11.476831436157227，loss cls : 0.09289836883544922,loss l2 : 3.7839133739471436,lr : 0.001\n",
            "Epoch 21/50:  99%|▉| 598/606 [02:00<00:01,  5.23im2021-03-26 17:45:26,381 train.py[line:353] INFO: Train step_6360: loss : 15.662261962890625,loss xy : 8.10110855102539,loss wh : 0.2656761705875397,loss obj : 5.305856227874756，loss cls : 1.9896198511123657,loss l2 : 1.8547773361206055,lr : 0.001\n",
            "Epoch 21/50: 100%|█| 606/606 [02:02<00:00,  5.18im2021-03-26 17:45:28,058 train.py[line:364] INFO: Checkpoint 21 saved !\n",
            "Epoch 21/50: 100%|█| 606/606 [02:02<00:00,  4.94im\n",
            "Epoch 22/50:   5%| | 32/606 [00:08<01:55,  4.96img2021-03-26 17:45:37,047 train.py[line:353] INFO: Train step_6380: loss : 12.16305923461914,loss xy : 5.977209091186523,loss wh : 0.17750811576843262,loss obj : 5.4458112716674805，loss cls : 0.5625301599502563,loss l2 : 1.680475115776062,lr : 0.001\n",
            "Epoch 22/50:  12%| | 72/606 [00:16<01:45,  5.06img2021-03-26 17:45:44,931 train.py[line:353] INFO: Train step_6400: loss : 19.096027374267578,loss xy : 10.007997512817383,loss wh : 0.2011011391878128,loss obj : 6.350147724151611，loss cls : 2.536781072616577,loss l2 : 2.4626572132110596,lr : 0.001\n",
            "Epoch 22/50:  18%|▏| 112/606 [00:24<01:38,  5.01im2021-03-26 17:45:52,885 train.py[line:353] INFO: Train step_6420: loss : 37.44257354736328,loss xy : 14.968742370605469,loss wh : 1.1709280014038086,loss obj : 19.4799747467041，loss cls : 1.822928786277771,loss l2 : 6.22398567199707,lr : 0.001\n",
            "Epoch 22/50:  25%|▎| 152/606 [00:32<01:30,  5.01im2021-03-26 17:46:00,814 train.py[line:353] INFO: Train step_6440: loss : 46.25859451293945,loss xy : 11.987573623657227,loss wh : 0.22152119874954224,loss obj : 23.86402702331543，loss cls : 10.185474395751953,loss l2 : 9.980110168457031,lr : 0.001\n",
            "Epoch 22/50:  32%|▎| 192/606 [00:40<01:21,  5.06im2021-03-26 17:46:08,723 train.py[line:353] INFO: Train step_6460: loss : 2.785792350769043,loss xy : 0.0,loss wh : 0.0,loss obj : 2.785792350769043，loss cls : 0.0,loss l2 : 0.010937278158962727,lr : 0.001\n",
            "Epoch 22/50:  38%|▍| 232/606 [00:48<01:14,  5.05im2021-03-26 17:46:16,622 train.py[line:353] INFO: Train step_6480: loss : 34.22055435180664,loss xy : 15.17286491394043,loss wh : 0.6938523650169373,loss obj : 18.086923599243164，loss cls : 0.266914963722229,loss l2 : 5.874017238616943,lr : 0.001\n",
            "Epoch 22/50:  45%|▍| 272/606 [00:56<01:06,  5.03im2021-03-26 17:46:24,560 train.py[line:353] INFO: Train step_6500: loss : 31.790176391601562,loss xy : 15.764050483703613,loss wh : 0.3822757303714752,loss obj : 14.625325202941895，loss cls : 1.0185247659683228,loss l2 : 5.207638263702393,lr : 0.001\n",
            "Epoch 22/50:  51%|▌| 312/606 [01:03<00:57,  5.09im2021-03-26 17:46:32,413 train.py[line:353] INFO: Train step_6520: loss : 9.315356254577637,loss xy : 6.05960750579834,loss wh : 0.03416282311081886,loss obj : 3.1618733406066895，loss cls : 0.059712011367082596,loss l2 : 0.5196206569671631,lr : 0.001\n",
            "Epoch 22/50:  58%|▌| 352/606 [01:11<00:49,  5.17im2021-03-26 17:46:40,251 train.py[line:353] INFO: Train step_6540: loss : 9.251067161560059,loss xy : 3.5234522819519043,loss wh : 0.22362792491912842,loss obj : 5.496280193328857，loss cls : 0.0077066062949597836,loss l2 : 1.436887264251709,lr : 0.001\n",
            "Epoch 22/50:  65%|▋| 392/606 [01:19<00:41,  5.10im2021-03-26 17:46:48,108 train.py[line:353] INFO: Train step_6560: loss : 36.406341552734375,loss xy : 24.46207618713379,loss wh : 1.2279466390609741,loss obj : 8.222383499145508，loss cls : 2.4939332008361816,loss l2 : 6.269497871398926,lr : 0.001\n",
            "Epoch 22/50:  71%|▋| 432/606 [01:27<00:34,  5.02im2021-03-26 17:46:56,006 train.py[line:353] INFO: Train step_6580: loss : 6.986704349517822,loss xy : 3.7430102825164795,loss wh : 0.19666966795921326,loss obj : 3.041553497314453，loss cls : 0.005470978561788797,loss l2 : 0.6595638990402222,lr : 0.001\n",
            "Epoch 22/50:  78%|▊| 472/606 [01:35<00:26,  5.07im2021-03-26 17:47:03,909 train.py[line:353] INFO: Train step_6600: loss : 47.2137336730957,loss xy : 23.11172103881836,loss wh : 1.1858410835266113,loss obj : 19.809167861938477，loss cls : 3.1070055961608887,loss l2 : 7.546972274780273,lr : 0.001\n",
            "Epoch 22/50:  84%|▊| 512/606 [01:43<00:18,  5.09im2021-03-26 17:47:11,777 train.py[line:353] INFO: Train step_6620: loss : 13.456981658935547,loss xy : 9.741479873657227,loss wh : 0.16934636235237122,loss obj : 3.5211520195007324，loss cls : 0.025003284215927124,loss l2 : 0.719578206539154,lr : 0.001\n",
            "Epoch 22/50:  91%|▉| 552/606 [01:51<00:10,  5.10im2021-03-26 17:47:19,651 train.py[line:353] INFO: Train step_6640: loss : 13.838690757751465,loss xy : 8.764893531799316,loss wh : 0.604644775390625,loss obj : 4.244992256164551，loss cls : 0.22416025400161743,loss l2 : 1.9424856901168823,lr : 0.001\n",
            "Epoch 22/50:  98%|▉| 592/606 [01:59<00:02,  5.20im2021-03-26 17:47:27,473 train.py[line:353] INFO: Train step_6660: loss : 16.124765396118164,loss xy : 6.12124490737915,loss wh : 0.12001273036003113,loss obj : 9.411438941955566，loss cls : 0.4720686376094818,loss l2 : 2.3208167552948,lr : 0.001\n",
            "Epoch 22/50: 100%|█| 606/606 [02:01<00:00,  5.20im2021-03-26 17:47:30,296 train.py[line:364] INFO: Checkpoint 22 saved !\n",
            "Epoch 22/50: 100%|█| 606/606 [02:02<00:00,  4.96im\n",
            "Epoch 23/50:   4%| | 26/606 [00:07<02:03,  4.68img2021-03-26 17:47:38,033 train.py[line:353] INFO: Train step_6680: loss : 51.37834930419922,loss xy : 20.249004364013672,loss wh : 1.8825523853302002,loss obj : 26.813844680786133，loss cls : 2.4329469203948975,loss l2 : 8.966994285583496,lr : 0.001\n",
            "Epoch 23/50:  11%| | 66/606 [00:15<01:45,  5.13img2021-03-26 17:47:45,943 train.py[line:353] INFO: Train step_6700: loss : 22.618688583374023,loss xy : 12.381128311157227,loss wh : 0.18451792001724243,loss obj : 8.795682907104492，loss cls : 1.2573603391647339,loss l2 : 3.1259026527404785,lr : 0.001\n",
            "Epoch 23/50:  17%|▏| 106/606 [00:23<01:38,  5.09im2021-03-26 17:47:53,815 train.py[line:353] INFO: Train step_6720: loss : 53.87367248535156,loss xy : 24.723180770874023,loss wh : 0.8817453384399414,loss obj : 16.335960388183594，loss cls : 11.932783126831055,loss l2 : 9.984237670898438,lr : 0.001\n",
            "Epoch 23/50:  24%|▏| 146/606 [00:31<01:30,  5.08im2021-03-26 17:48:01,727 train.py[line:353] INFO: Train step_6740: loss : 49.579627990722656,loss xy : 17.528282165527344,loss wh : 1.2033032178878784,loss obj : 27.384502410888672，loss cls : 3.4635398387908936,loss l2 : 9.687461853027344,lr : 0.001\n",
            "Epoch 23/50:  31%|▎| 186/606 [00:38<01:22,  5.09im2021-03-26 17:48:09,641 train.py[line:353] INFO: Train step_6760: loss : 37.61571502685547,loss xy : 14.021932601928711,loss wh : 1.2740848064422607,loss obj : 15.675283432006836，loss cls : 6.644413471221924,loss l2 : 7.990119934082031,lr : 0.001\n",
            "Epoch 23/50:  37%|▎| 226/606 [00:46<01:14,  5.12im2021-03-26 17:48:17,519 train.py[line:353] INFO: Train step_6780: loss : 23.55927848815918,loss xy : 13.328627586364746,loss wh : 0.3541097939014435,loss obj : 9.497466087341309，loss cls : 0.37907642126083374,loss l2 : 3.1019489765167236,lr : 0.001\n",
            "Epoch 23/50:  44%|▍| 266/606 [00:54<01:07,  5.07im2021-03-26 17:48:25,404 train.py[line:353] INFO: Train step_6800: loss : 38.5098762512207,loss xy : 16.869985580444336,loss wh : 1.3233106136322021,loss obj : 18.99576759338379，loss cls : 1.320812463760376,loss l2 : 8.31297492980957,lr : 0.001\n",
            "Epoch 23/50:  50%|▌| 306/606 [01:02<00:58,  5.16im2021-03-26 17:48:33,261 train.py[line:353] INFO: Train step_6820: loss : 30.84912872314453,loss xy : 23.29351806640625,loss wh : 0.5650806427001953,loss obj : 6.936738014221191，loss cls : 0.05379176884889603,loss l2 : 2.762429714202881,lr : 0.001\n",
            "Epoch 23/50:  57%|▌| 346/606 [01:10<00:52,  5.00im2021-03-26 17:48:41,180 train.py[line:353] INFO: Train step_6840: loss : 20.402318954467773,loss xy : 15.20633316040039,loss wh : 0.725702702999115,loss obj : 4.248989582061768，loss cls : 0.22129416465759277,loss l2 : 2.2721142768859863,lr : 0.001\n",
            "Epoch 23/50:  64%|▋| 386/606 [01:18<00:44,  4.97im2021-03-26 17:48:49,136 train.py[line:353] INFO: Train step_6860: loss : 25.334423065185547,loss xy : 14.218432426452637,loss wh : 0.2655791938304901,loss obj : 10.826498985290527，loss cls : 0.023913126438856125,loss l2 : 3.580827236175537,lr : 0.001\n",
            "Epoch 23/50:  70%|▋| 426/606 [01:26<00:35,  5.10im2021-03-26 17:48:57,044 train.py[line:353] INFO: Train step_6880: loss : 39.71878433227539,loss xy : 11.15101432800293,loss wh : 1.356815218925476,loss obj : 24.762277603149414，loss cls : 2.4486775398254395,loss l2 : 9.18743896484375,lr : 0.001\n",
            "Epoch 23/50:  77%|▊| 466/606 [01:34<00:27,  5.08im2021-03-26 17:49:04,923 train.py[line:353] INFO: Train step_6900: loss : 51.92137145996094,loss xy : 19.807022094726562,loss wh : 0.9464102387428284,loss obj : 30.746341705322266，loss cls : 0.42159542441368103,loss l2 : 9.496524810791016,lr : 0.001\n",
            "Epoch 23/50:  83%|▊| 506/606 [01:42<00:19,  5.08im2021-03-26 17:49:12,818 train.py[line:353] INFO: Train step_6920: loss : 30.481325149536133,loss xy : 18.02366065979004,loss wh : 0.6408625245094299,loss obj : 11.658126831054688，loss cls : 0.15867452323436737,loss l2 : 4.595187664031982,lr : 0.001\n",
            "Epoch 23/50:  90%|▉| 546/606 [01:50<00:11,  5.13im2021-03-26 17:49:20,710 train.py[line:353] INFO: Train step_6940: loss : 43.10063552856445,loss xy : 16.53980827331543,loss wh : 1.775310754776001,loss obj : 24.397722244262695，loss cls : 0.38779377937316895,loss l2 : 9.11871337890625,lr : 0.001\n",
            "Epoch 23/50:  97%|▉| 586/606 [01:57<00:03,  5.21im2021-03-26 17:49:28,502 train.py[line:353] INFO: Train step_6960: loss : 51.06999206542969,loss xy : 23.981386184692383,loss wh : 1.5970574617385864,loss obj : 24.521533966064453，loss cls : 0.9700177907943726,loss l2 : 8.664398193359375,lr : 0.001\n",
            "Epoch 23/50: 100%|█| 606/606 [02:01<00:00,  5.23im2021-03-26 17:49:32,478 train.py[line:364] INFO: Checkpoint 23 saved !\n",
            "Epoch 23/50: 100%|█| 606/606 [02:02<00:00,  4.96im\n",
            "Epoch 24/50:   3%| | 20/606 [00:06<02:24,  4.05img2021-03-26 17:49:39,156 train.py[line:353] INFO: Train step_6980: loss : 30.611576080322266,loss xy : 10.5013427734375,loss wh : 1.466572880744934,loss obj : 13.507786750793457，loss cls : 5.135873794555664,loss l2 : 7.389730453491211,lr : 0.001\n",
            "Epoch 24/50:  10%| | 60/606 [00:14<01:47,  5.09img2021-03-26 17:49:47,050 train.py[line:353] INFO: Train step_7000: loss : 20.527294158935547,loss xy : 12.445734024047852,loss wh : 0.5211174488067627,loss obj : 5.5268049240112305，loss cls : 2.033637046813965,loss l2 : 2.8980753421783447,lr : 0.001\n",
            "Epoch 24/50:  17%|▏| 100/606 [00:22<01:40,  5.03im2021-03-26 17:49:54,976 train.py[line:353] INFO: Train step_7020: loss : 15.243507385253906,loss xy : 9.621524810791016,loss wh : 0.17723673582077026,loss obj : 4.9505743980407715，loss cls : 0.494171142578125,loss l2 : 1.584316611289978,lr : 0.001\n",
            "Epoch 24/50:  23%|▏| 140/606 [00:30<01:32,  5.05im2021-03-26 17:50:02,968 train.py[line:353] INFO: Train step_7040: loss : 16.53750991821289,loss xy : 10.589958190917969,loss wh : 0.23680108785629272,loss obj : 4.456648349761963，loss cls : 1.254103660583496,loss l2 : 1.8848564624786377,lr : 0.001\n",
            "Epoch 24/50:  30%|▎| 180/606 [00:38<01:25,  4.99im2021-03-26 17:50:10,935 train.py[line:353] INFO: Train step_7060: loss : 41.131317138671875,loss xy : 13.214783668518066,loss wh : 0.9657256603240967,loss obj : 19.54460334777832，loss cls : 7.406206130981445,loss l2 : 7.470093250274658,lr : 0.001\n",
            "Epoch 24/50:  36%|▎| 220/606 [00:45<01:16,  5.07im2021-03-26 17:50:18,870 train.py[line:353] INFO: Train step_7080: loss : 23.323257446289062,loss xy : 15.008512496948242,loss wh : 0.9303684234619141,loss obj : 6.839216232299805，loss cls : 0.5451594591140747,loss l2 : 3.7774953842163086,lr : 0.001\n",
            "Epoch 24/50:  43%|▍| 260/606 [00:53<01:07,  5.15im2021-03-26 17:50:26,748 train.py[line:353] INFO: Train step_7100: loss : 17.140766143798828,loss xy : 8.48270034790039,loss wh : 0.2923821210861206,loss obj : 8.339995384216309，loss cls : 0.025688260793685913,loss l2 : 2.661062717437744,lr : 0.001\n",
            "Epoch 24/50:  50%|▍| 300/606 [01:01<01:00,  5.07im2021-03-26 17:50:34,700 train.py[line:353] INFO: Train step_7120: loss : 57.65129852294922,loss xy : 21.260316848754883,loss wh : 3.1185412406921387,loss obj : 30.87779426574707，loss cls : 2.394646406173706,loss l2 : 13.527412414550781,lr : 0.001\n",
            "Epoch 24/50:  56%|▌| 340/606 [01:09<00:52,  5.06im2021-03-26 17:50:42,568 train.py[line:353] INFO: Train step_7140: loss : 9.51702880859375,loss xy : 5.767165184020996,loss wh : 0.09166848659515381,loss obj : 3.6328179836273193，loss cls : 0.025376884266734123,loss l2 : 0.9269548654556274,lr : 0.001\n",
            "Epoch 24/50:  63%|▋| 380/606 [01:17<00:44,  5.04im2021-03-26 17:50:50,454 train.py[line:353] INFO: Train step_7160: loss : 5.24917459487915,loss xy : 2.1509268283843994,loss wh : 0.17427733540534973,loss obj : 2.9167919158935547，loss cls : 0.007178925909101963,loss l2 : 0.7337056398391724,lr : 0.001\n",
            "Epoch 24/50:  69%|▋| 420/606 [01:25<00:36,  5.07im2021-03-26 17:50:58,323 train.py[line:353] INFO: Train step_7180: loss : 13.997777938842773,loss xy : 7.04331111907959,loss wh : 0.38749170303344727,loss obj : 6.538683891296387，loss cls : 0.02829216606914997,loss l2 : 2.1606881618499756,lr : 0.001\n",
            "Epoch 24/50:  76%|▊| 460/606 [01:33<00:29,  5.03im2021-03-26 17:51:06,263 train.py[line:353] INFO: Train step_7200: loss : 18.161149978637695,loss xy : 12.052677154541016,loss wh : 0.09217961132526398,loss obj : 3.402073860168457，loss cls : 2.614219903945923,loss l2 : 1.6728099584579468,lr : 0.001\n",
            "Epoch 24/50:  83%|▊| 500/606 [01:41<00:20,  5.10im2021-03-26 17:51:14,081 train.py[line:353] INFO: Train step_7220: loss : 19.35837173461914,loss xy : 9.16873836517334,loss wh : 0.440463662147522,loss obj : 6.907445907592773，loss cls : 2.841724157333374,loss l2 : 3.5709826946258545,lr : 0.001\n",
            "Epoch 24/50:  89%|▉| 540/606 [01:49<00:13,  5.04im2021-03-26 17:51:22,012 train.py[line:353] INFO: Train step_7240: loss : 11.207188606262207,loss xy : 6.271411895751953,loss wh : 0.2005881667137146,loss obj : 4.391977310180664，loss cls : 0.34321075677871704,loss l2 : 0.8922579884529114,lr : 0.001\n",
            "Epoch 24/50:  96%|▉| 580/606 [01:57<00:05,  5.16im2021-03-26 17:51:29,894 train.py[line:353] INFO: Train step_7260: loss : 14.800005912780762,loss xy : 8.051263809204102,loss wh : 1.0339032411575317,loss obj : 5.5128936767578125，loss cls : 0.2019452601671219,loss l2 : 3.4757020473480225,lr : 0.001\n",
            "Epoch 24/50: 100%|█| 606/606 [02:02<00:00,  5.23im2021-03-26 17:51:34,991 train.py[line:364] INFO: Checkpoint 24 saved !\n",
            "Epoch 24/50: 100%|█| 606/606 [02:02<00:00,  4.95im\n",
            "Epoch 25/50:   2%| | 14/606 [00:05<03:23,  2.91img2021-03-26 17:51:40,741 train.py[line:353] INFO: Train step_7280: loss : 22.479167938232422,loss xy : 6.316298484802246,loss wh : 0.41940101981163025,loss obj : 10.90461540222168，loss cls : 4.838852405548096,loss l2 : 5.5152201652526855,lr : 0.001\n",
            "Epoch 25/50:   9%| | 54/606 [00:13<01:49,  5.04img2021-03-26 17:51:48,671 train.py[line:353] INFO: Train step_7300: loss : 47.040802001953125,loss xy : 22.937889099121094,loss wh : 2.2941932678222656,loss obj : 21.057735443115234，loss cls : 0.7509828805923462,loss l2 : 8.013448715209961,lr : 0.001\n",
            "Epoch 25/50:  16%|▏| 94/606 [00:21<01:42,  5.00img2021-03-26 17:51:56,644 train.py[line:353] INFO: Train step_7320: loss : 22.94172477722168,loss xy : 13.832575798034668,loss wh : 1.1101895570755005,loss obj : 7.927148818969727，loss cls : 0.07180975377559662,loss l2 : 4.153642654418945,lr : 0.001\n",
            "Epoch 25/50:  22%|▏| 134/606 [00:29<01:33,  5.05im2021-03-26 17:52:04,606 train.py[line:353] INFO: Train step_7340: loss : 46.77355194091797,loss xy : 17.72593116760254,loss wh : 1.0539588928222656,loss obj : 19.908435821533203，loss cls : 8.085229873657227,loss l2 : 8.841483116149902,lr : 0.001\n",
            "Epoch 25/50:  29%|▎| 174/606 [00:37<01:26,  5.01im2021-03-26 17:52:12,571 train.py[line:353] INFO: Train step_7360: loss : 28.491058349609375,loss xy : 12.758822441101074,loss wh : 1.2408045530319214,loss obj : 14.325850486755371，loss cls : 0.16558010876178741,loss l2 : 5.117855548858643,lr : 0.001\n",
            "Epoch 25/50:  35%|▎| 214/606 [00:45<01:17,  5.04im2021-03-26 17:52:20,513 train.py[line:353] INFO: Train step_7380: loss : 22.05069923400879,loss xy : 13.32313346862793,loss wh : 0.43995457887649536,loss obj : 7.675811767578125，loss cls : 0.611798882484436,loss l2 : 3.5654053688049316,lr : 0.001\n",
            "Epoch 25/50:  42%|▍| 254/606 [00:53<01:08,  5.12im2021-03-26 17:52:28,466 train.py[line:353] INFO: Train step_7400: loss : 17.807628631591797,loss xy : 10.023427963256836,loss wh : 0.619174599647522,loss obj : 6.857396602630615，loss cls : 0.3076290488243103,loss l2 : 3.406893014907837,lr : 0.001\n",
            "Epoch 25/50:  49%|▍| 294/606 [01:00<01:00,  5.13im2021-03-26 17:52:36,344 train.py[line:353] INFO: Train step_7420: loss : 13.352444648742676,loss xy : 7.623217582702637,loss wh : 0.2556402087211609,loss obj : 4.927922248840332，loss cls : 0.5456646680831909,loss l2 : 1.875572681427002,lr : 0.001\n",
            "Epoch 25/50:  55%|▌| 334/606 [01:08<00:54,  5.02im2021-03-26 17:52:44,278 train.py[line:353] INFO: Train step_7440: loss : 15.504971504211426,loss xy : 8.97932243347168,loss wh : 0.2107437551021576,loss obj : 3.7651658058166504，loss cls : 2.549738883972168,loss l2 : 2.2064805030822754,lr : 0.001\n",
            "Epoch 25/50:  62%|▌| 374/606 [01:16<00:46,  5.03im2021-03-26 17:52:52,226 train.py[line:353] INFO: Train step_7460: loss : 23.422231674194336,loss xy : 8.849496841430664,loss wh : 0.5124199986457825,loss obj : 12.237762451171875，loss cls : 1.8225542306900024,loss l2 : 3.097071647644043,lr : 0.001\n",
            "Epoch 25/50:  68%|▋| 414/606 [01:24<00:38,  5.02im2021-03-26 17:53:00,183 train.py[line:353] INFO: Train step_7480: loss : 17.46155548095703,loss xy : 8.438915252685547,loss wh : 0.4087826907634735,loss obj : 8.588027000427246，loss cls : 0.02583000250160694,loss l2 : 3.5952184200286865,lr : 0.001\n",
            "Epoch 25/50:  75%|▋| 454/606 [01:32<00:29,  5.12im2021-03-26 17:53:08,103 train.py[line:353] INFO: Train step_7500: loss : 41.56352233886719,loss xy : 14.13057804107666,loss wh : 2.9658203125,loss obj : 15.251144409179688，loss cls : 9.215982437133789,loss l2 : 11.56726360321045,lr : 0.001\n",
            "Epoch 25/50:  82%|▊| 494/606 [01:40<00:22,  5.08im2021-03-26 17:53:16,048 train.py[line:353] INFO: Train step_7520: loss : 14.840289115905762,loss xy : 3.7604713439941406,loss wh : 0.6766788363456726,loss obj : 8.610635757446289，loss cls : 1.7925031185150146,loss l2 : 4.234936714172363,lr : 0.001\n",
            "Epoch 25/50:  88%|▉| 534/606 [01:48<00:14,  5.08im2021-03-26 17:53:23,950 train.py[line:353] INFO: Train step_7540: loss : 41.000816345214844,loss xy : 20.09286880493164,loss wh : 0.8512073755264282,loss obj : 17.689102172851562，loss cls : 2.367637872695923,loss l2 : 6.281121730804443,lr : 0.001\n",
            "Epoch 25/50:  95%|▉| 574/606 [01:56<00:06,  5.06im2021-03-26 17:53:31,905 train.py[line:353] INFO: Train step_7560: loss : 17.26516342163086,loss xy : 7.86180305480957,loss wh : 0.16853205859661102,loss obj : 8.575740814208984，loss cls : 0.6590874791145325,loss l2 : 2.6355834007263184,lr : 0.001\n",
            "Epoch 25/50: 100%|█| 606/606 [02:02<00:00,  5.22im2021-03-26 17:53:38,218 train.py[line:364] INFO: Checkpoint 25 saved !\n",
            "Epoch 25/50: 100%|█| 606/606 [02:03<00:00,  4.92im\n",
            "Epoch 26/50:   1%| | 8/606 [00:03<05:52,  1.70img/2021-03-26 17:53:42,586 train.py[line:353] INFO: Train step_7580: loss : 38.18926239013672,loss xy : 23.409828186035156,loss wh : 0.7474307417869568,loss obj : 13.093420028686523，loss cls : 0.9385827779769897,loss l2 : 5.2914557456970215,lr : 0.001\n",
            "Epoch 26/50:   8%| | 48/606 [00:11<01:48,  5.15img2021-03-26 17:53:50,485 train.py[line:353] INFO: Train step_7600: loss : 32.92443084716797,loss xy : 21.057083129882812,loss wh : 0.770451545715332,loss obj : 9.664023399353027，loss cls : 1.4328726530075073,loss l2 : 4.721957683563232,lr : 0.001\n",
            "Epoch 26/50:  15%|▏| 88/606 [00:19<01:42,  5.05img2021-03-26 17:53:58,457 train.py[line:353] INFO: Train step_7620: loss : 25.16958236694336,loss xy : 12.563486099243164,loss wh : 0.67934250831604,loss obj : 11.602823257446289，loss cls : 0.323930561542511,loss l2 : 3.944293260574341,lr : 0.001\n",
            "Epoch 26/50:  21%|▏| 128/606 [00:27<01:33,  5.10im2021-03-26 17:54:06,422 train.py[line:353] INFO: Train step_7640: loss : 26.257423400878906,loss xy : 14.010162353515625,loss wh : 0.9869296550750732,loss obj : 10.79133415222168，loss cls : 0.4689982235431671,loss l2 : 5.51082181930542,lr : 0.001\n",
            "Epoch 26/50:  28%|▎| 168/606 [00:35<01:27,  5.01im2021-03-26 17:54:14,398 train.py[line:353] INFO: Train step_7660: loss : 55.8870849609375,loss xy : 31.34991455078125,loss wh : 0.8753095865249634,loss obj : 20.561262130737305，loss cls : 3.1006016731262207,loss l2 : 7.55068302154541,lr : 0.001\n",
            "Epoch 26/50:  34%|▎| 208/606 [00:43<01:19,  5.00im2021-03-26 17:54:22,336 train.py[line:353] INFO: Train step_7680: loss : 71.45447540283203,loss xy : 41.289276123046875,loss wh : 3.622757911682129,loss obj : 25.705154418945312，loss cls : 0.8372894525527954,loss l2 : 16.83097267150879,lr : 0.001\n",
            "Epoch 26/50:  41%|▍| 248/606 [00:51<01:11,  5.04im2021-03-26 17:54:30,305 train.py[line:353] INFO: Train step_7700: loss : 19.824953079223633,loss xy : 9.434329986572266,loss wh : 0.5969350934028625,loss obj : 8.213580131530762，loss cls : 1.5801060199737549,loss l2 : 3.0667026042938232,lr : 0.001\n",
            "Epoch 26/50:  48%|▍| 288/606 [00:59<01:02,  5.09im2021-03-26 17:54:38,222 train.py[line:353] INFO: Train step_7720: loss : 63.60062026977539,loss xy : 22.347143173217773,loss wh : 1.9195806980133057,loss obj : 32.02622604370117，loss cls : 7.307669639587402,loss l2 : 12.35621166229248,lr : 0.001\n",
            "Epoch 26/50:  54%|▌| 328/606 [01:07<00:55,  4.99im2021-03-26 17:54:46,175 train.py[line:353] INFO: Train step_7740: loss : 9.96170425415039,loss xy : 7.3422346115112305,loss wh : 0.08550834655761719,loss obj : 2.411471366882324，loss cls : 0.12249032407999039,loss l2 : 0.47431081533432007,lr : 0.001\n",
            "Epoch 26/50:  61%|▌| 368/606 [01:15<00:47,  5.00im2021-03-26 17:54:54,119 train.py[line:353] INFO: Train step_7760: loss : 32.09287643432617,loss xy : 19.34951400756836,loss wh : 0.750625729560852,loss obj : 11.785171508789062，loss cls : 0.20756688714027405,loss l2 : 4.833028316497803,lr : 0.001\n",
            "Epoch 26/50:  67%|▋| 408/606 [01:23<00:39,  5.00im2021-03-26 17:55:02,101 train.py[line:353] INFO: Train step_7780: loss : 12.870400428771973,loss xy : 7.686539649963379,loss wh : 0.42512404918670654,loss obj : 4.648658752441406，loss cls : 0.11007823050022125,loss l2 : 2.0584564208984375,lr : 0.001\n",
            "Epoch 26/50:  74%|▋| 448/606 [01:31<00:30,  5.19im2021-03-26 17:55:09,950 train.py[line:353] INFO: Train step_7800: loss : 22.277191162109375,loss xy : 11.819211959838867,loss wh : 0.3318173289299011,loss obj : 9.848714828491211，loss cls : 0.2774464190006256,loss l2 : 2.5396714210510254,lr : 0.001\n",
            "Epoch 26/50:  81%|▊| 488/606 [01:39<00:23,  5.09im2021-03-26 17:55:17,808 train.py[line:353] INFO: Train step_7820: loss : 19.777706146240234,loss xy : 11.869115829467773,loss wh : 0.2857436239719391,loss obj : 7.594968795776367，loss cls : 0.02787816897034645,loss l2 : 2.8980236053466797,lr : 0.001\n",
            "Epoch 26/50:  87%|▊| 528/606 [01:47<00:15,  5.04im2021-03-26 17:55:25,767 train.py[line:353] INFO: Train step_7840: loss : 15.130890846252441,loss xy : 11.247878074645996,loss wh : 0.251468688249588,loss obj : 3.5241637229919434，loss cls : 0.10738076269626617,loss l2 : 0.8746423721313477,lr : 0.001\n",
            "Epoch 26/50:  94%|▉| 568/606 [01:55<00:07,  5.04im2021-03-26 17:55:33,718 train.py[line:353] INFO: Train step_7860: loss : 7.231955528259277,loss xy : 4.881892681121826,loss wh : 0.1260681450366974,loss obj : 2.202216863632202，loss cls : 0.021778222173452377,loss l2 : 0.4904743432998657,lr : 0.001\n",
            "Epoch 26/50: 100%|█| 606/606 [02:02<00:00,  5.19im2021-03-26 17:55:41,233 train.py[line:364] INFO: Checkpoint 26 saved !\n",
            "Epoch 26/50: 100%|█| 606/606 [02:03<00:00,  4.93im\n",
            "Epoch 27/50:   0%| | 2/606 [00:01<09:52,  1.02img/2021-03-26 17:55:44,253 train.py[line:353] INFO: Train step_7880: loss : 27.59024429321289,loss xy : 15.509113311767578,loss wh : 0.31798380613327026,loss obj : 10.1628999710083，loss cls : 1.6002458333969116,loss l2 : 4.312591552734375,lr : 0.001\n",
            "Epoch 27/50:   7%| | 42/606 [00:10<01:51,  5.06img2021-03-26 17:55:52,306 train.py[line:353] INFO: Train step_7900: loss : 18.83905792236328,loss xy : 8.382098197937012,loss wh : 0.6961278319358826,loss obj : 4.532815933227539，loss cls : 5.228014945983887,loss l2 : 4.118565559387207,lr : 0.001\n",
            "Epoch 27/50:  14%|▏| 82/606 [00:18<01:45,  4.95img2021-03-26 17:56:00,296 train.py[line:353] INFO: Train step_7920: loss : 40.50381088256836,loss xy : 19.12306022644043,loss wh : 1.2785913944244385,loss obj : 15.459147453308105，loss cls : 4.6430134773254395,loss l2 : 7.40023946762085,lr : 0.001\n",
            "Epoch 27/50:  20%|▏| 122/606 [00:26<01:36,  5.02im2021-03-26 17:56:08,216 train.py[line:353] INFO: Train step_7940: loss : 51.90351104736328,loss xy : 28.802818298339844,loss wh : 1.6216648817062378,loss obj : 18.004365921020508，loss cls : 3.474663019180298,loss l2 : 8.182754516601562,lr : 0.001\n",
            "Epoch 27/50:  27%|▎| 162/606 [00:34<01:28,  5.02im2021-03-26 17:56:16,181 train.py[line:353] INFO: Train step_7960: loss : 13.242630004882812,loss xy : 9.072035789489746,loss wh : 0.10449045896530151,loss obj : 2.3357880115509033，loss cls : 1.7303160429000854,loss l2 : 1.2890770435333252,lr : 0.001\n",
            "Epoch 27/50:  33%|▎| 202/606 [00:42<01:20,  4.99im2021-03-26 17:56:24,143 train.py[line:353] INFO: Train step_7980: loss : 18.637962341308594,loss xy : 10.477615356445312,loss wh : 0.4668841063976288,loss obj : 7.65576171875，loss cls : 0.03770070523023605,loss l2 : 2.5462687015533447,lr : 0.001\n",
            "Epoch 27/50:  40%|▍| 242/606 [00:50<01:11,  5.06im2021-03-26 17:56:32,071 train.py[line:353] INFO: Train step_8000: loss : 43.95909881591797,loss xy : 23.857402801513672,loss wh : 1.385472297668457,loss obj : 14.440696716308594，loss cls : 4.275528430938721,loss l2 : 7.757963180541992,lr : 0.001\n",
            "Epoch 27/50:  47%|▍| 282/606 [00:58<01:04,  5.02im2021-03-26 17:56:39,997 train.py[line:353] INFO: Train step_8020: loss : 24.66969871520996,loss xy : 20.065412521362305,loss wh : 0.19705502688884735,loss obj : 4.354093074798584，loss cls : 0.053135912865400314,loss l2 : 2.133511543273926,lr : 0.001\n",
            "Epoch 27/50:  53%|▌| 322/606 [01:06<00:56,  5.05im2021-03-26 17:56:47,914 train.py[line:353] INFO: Train step_8040: loss : 26.22408103942871,loss xy : 15.74023151397705,loss wh : 1.115211009979248,loss obj : 9.256889343261719，loss cls : 0.11174996942281723,loss l2 : 4.67343807220459,lr : 0.001\n",
            "Epoch 27/50:  60%|▌| 362/606 [01:14<00:49,  4.98im2021-03-26 17:56:55,920 train.py[line:353] INFO: Train step_8060: loss : 7.592125415802002,loss xy : 3.5144479274749756,loss wh : 0.01041752751916647,loss obj : 4.062053203582764，loss cls : 0.005206417292356491,loss l2 : 0.977621853351593,lr : 0.001\n",
            "Epoch 27/50:  66%|▋| 402/606 [01:22<00:40,  5.04im2021-03-26 17:57:03,851 train.py[line:353] INFO: Train step_8080: loss : 13.042272567749023,loss xy : 6.935863971710205,loss wh : 0.1953795850276947,loss obj : 5.904426574707031，loss cls : 0.006602258421480656,loss l2 : 2.1420738697052,lr : 0.001\n",
            "Epoch 27/50:  73%|▋| 442/606 [01:30<00:31,  5.13im2021-03-26 17:57:11,747 train.py[line:353] INFO: Train step_8100: loss : 33.254722595214844,loss xy : 20.19504737854004,loss wh : 1.189335584640503,loss obj : 11.018209457397461，loss cls : 0.8521317839622498,loss l2 : 6.6472649574279785,lr : 0.001\n",
            "Epoch 27/50:  80%|▊| 482/606 [01:38<00:24,  5.10im2021-03-26 17:57:19,648 train.py[line:353] INFO: Train step_8120: loss : 8.530061721801758,loss xy : 6.426721572875977,loss wh : 0.3157457113265991,loss obj : 1.5154495239257812，loss cls : 0.2721443772315979,loss l2 : 0.7843414545059204,lr : 0.001\n",
            "Epoch 27/50:  86%|▊| 522/606 [01:45<00:16,  4.98im2021-03-26 17:57:27,580 train.py[line:353] INFO: Train step_8140: loss : 9.748881340026855,loss xy : 7.491087913513184,loss wh : 0.1673058271408081,loss obj : 2.0560760498046875，loss cls : 0.03441176190972328,loss l2 : 0.5960332155227661,lr : 0.001\n",
            "Epoch 27/50:  93%|▉| 562/606 [01:53<00:08,  5.04im2021-03-26 17:57:35,479 train.py[line:353] INFO: Train step_8160: loss : 10.676164627075195,loss xy : 7.058740139007568,loss wh : 0.047989509999752045,loss obj : 3.557760715484619，loss cls : 0.011674717999994755,loss l2 : 0.922573983669281,lr : 0.001\n",
            "Epoch 27/50:  99%|▉| 602/606 [02:01<00:00,  5.24im2021-03-26 17:57:43,146 train.py[line:353] INFO: Train step_8180: loss : 27.246980667114258,loss xy : 12.862940788269043,loss wh : 0.545058012008667,loss obj : 13.596853256225586，loss cls : 0.2421274483203888,loss l2 : 4.131330490112305,lr : 0.001\n",
            "Epoch 27/50: 100%|█| 606/606 [02:02<00:00,  5.24im2021-03-26 17:57:44,039 train.py[line:364] INFO: Checkpoint 27 saved !\n",
            "Epoch 27/50: 100%|█| 606/606 [02:02<00:00,  4.93im\n",
            "Epoch 28/50:   6%| | 36/606 [00:09<01:53,  5.03img2021-03-26 17:57:54,027 train.py[line:353] INFO: Train step_8200: loss : 17.115737915039062,loss xy : 9.419801712036133,loss wh : 0.3294314742088318,loss obj : 7.346181869506836，loss cls : 0.020322291180491447,loss l2 : 3.0079092979431152,lr : 0.001\n",
            "Epoch 28/50:  13%|▏| 76/606 [00:17<01:46,  4.96img2021-03-26 17:58:02,002 train.py[line:353] INFO: Train step_8220: loss : 5.760010719299316,loss xy : 4.2386956214904785,loss wh : 0.0526350736618042,loss obj : 1.4669585227966309，loss cls : 0.0017211635131388903,loss l2 : 0.4991275668144226,lr : 0.001\n",
            "Epoch 28/50:  19%|▏| 116/606 [00:25<01:37,  5.03im2021-03-26 17:58:09,965 train.py[line:353] INFO: Train step_8240: loss : 21.62543296813965,loss xy : 12.826887130737305,loss wh : 0.8145012855529785,loss obj : 7.5747809410095215，loss cls : 0.40926414728164673,loss l2 : 3.7069597244262695,lr : 0.001\n",
            "Epoch 28/50:  26%|▎| 156/606 [00:33<01:29,  5.04im2021-03-26 17:58:17,847 train.py[line:353] INFO: Train step_8260: loss : 6.13239049911499,loss xy : 3.798872947692871,loss wh : 0.07992125302553177,loss obj : 2.24542498588562，loss cls : 0.00817152950912714,loss l2 : 0.5863444209098816,lr : 0.001\n",
            "Epoch 28/50:  32%|▎| 196/606 [00:41<01:21,  5.04im2021-03-26 17:58:25,760 train.py[line:353] INFO: Train step_8280: loss : 16.116191864013672,loss xy : 11.694551467895508,loss wh : 0.28713342547416687,loss obj : 3.4590847492218018，loss cls : 0.6754229664802551,loss l2 : 1.8568470478057861,lr : 0.001\n",
            "Epoch 28/50:  39%|▍| 236/606 [00:49<01:13,  5.01im2021-03-26 17:58:33,724 train.py[line:353] INFO: Train step_8300: loss : 27.069782257080078,loss xy : 17.759370803833008,loss wh : 0.5022282600402832,loss obj : 8.723848342895508，loss cls : 0.08433586359024048,loss l2 : 3.7055764198303223,lr : 0.001\n",
            "Epoch 28/50:  46%|▍| 276/606 [00:57<01:05,  5.05im2021-03-26 17:58:41,619 train.py[line:353] INFO: Train step_8320: loss : 33.743778228759766,loss xy : 13.59163761138916,loss wh : 0.42513415217399597,loss obj : 19.528722763061523，loss cls : 0.19828340411186218,loss l2 : 6.36879825592041,lr : 0.001\n",
            "Epoch 28/50:  52%|▌| 316/606 [01:05<00:57,  5.01im2021-03-26 17:58:49,594 train.py[line:353] INFO: Train step_8340: loss : 32.54803466796875,loss xy : 16.291259765625,loss wh : 1.2878698110580444,loss obj : 11.806105613708496，loss cls : 3.162799596786499,loss l2 : 5.95939302444458,lr : 0.001\n",
            "Epoch 28/50:  59%|▌| 356/606 [01:13<00:49,  5.03im2021-03-26 17:58:57,595 train.py[line:353] INFO: Train step_8360: loss : 57.5571174621582,loss xy : 16.12529182434082,loss wh : 3.901505470275879,loss obj : 31.210939407348633，loss cls : 6.319378852844238,loss l2 : 14.959721565246582,lr : 0.001\n",
            "Epoch 28/50:  65%|▋| 396/606 [01:21<00:41,  5.03im2021-03-26 17:59:05,547 train.py[line:353] INFO: Train step_8380: loss : 24.566070556640625,loss xy : 18.139511108398438,loss wh : 0.31896358728408813,loss obj : 4.6072893142700195，loss cls : 1.500305414199829,loss l2 : 2.774773359298706,lr : 0.001\n",
            "Epoch 28/50:  72%|▋| 436/606 [01:29<00:33,  5.04im2021-03-26 17:59:13,465 train.py[line:353] INFO: Train step_8400: loss : 17.57221794128418,loss xy : 9.934640884399414,loss wh : 0.3342580497264862,loss obj : 7.073671817779541，loss cls : 0.22964580357074738,loss l2 : 2.2350552082061768,lr : 0.001\n",
            "Epoch 28/50:  79%|▊| 476/606 [01:36<00:25,  5.09im2021-03-26 17:59:21,373 train.py[line:353] INFO: Train step_8420: loss : 17.078691482543945,loss xy : 10.836652755737305,loss wh : 0.35472679138183594,loss obj : 2.6438755989074707，loss cls : 3.243436574935913,loss l2 : 2.733203887939453,lr : 0.001\n",
            "Epoch 28/50:  85%|▊| 516/606 [01:44<00:17,  5.09im2021-03-26 17:59:29,250 train.py[line:353] INFO: Train step_8440: loss : 17.452852249145508,loss xy : 6.6665544509887695,loss wh : 0.7035682797431946,loss obj : 9.961893081665039，loss cls : 0.12083658576011658,loss l2 : 3.4431660175323486,lr : 0.001\n",
            "Epoch 28/50:  92%|▉| 556/606 [01:52<00:09,  5.09im2021-03-26 17:59:37,226 train.py[line:353] INFO: Train step_8460: loss : 30.214162826538086,loss xy : 22.862585067749023,loss wh : 0.3803509771823883,loss obj : 6.854668617248535，loss cls : 0.11656017601490021,loss l2 : 3.0555107593536377,lr : 0.001\n",
            "Epoch 28/50:  98%|▉| 596/606 [02:00<00:01,  5.21im2021-03-26 17:59:45,044 train.py[line:353] INFO: Train step_8480: loss : 63.09756851196289,loss xy : 27.137039184570312,loss wh : 11.63630199432373,loss obj : 17.966976165771484，loss cls : 6.3572516441345215,loss l2 : 31.72710609436035,lr : 0.001\n",
            "Epoch 28/50: 100%|█| 606/606 [02:02<00:00,  5.21im2021-03-26 17:59:47,115 train.py[line:364] INFO: Checkpoint 28 saved !\n",
            "Epoch 28/50: 100%|█| 606/606 [02:03<00:00,  4.92im\n",
            "Epoch 29/50:   5%| | 30/606 [00:08<01:58,  4.84img2021-03-26 17:59:55,948 train.py[line:353] INFO: Train step_8500: loss : 30.906604766845703,loss xy : 14.873287200927734,loss wh : 0.3488864004611969,loss obj : 15.192191123962402，loss cls : 0.49224090576171875,loss l2 : 3.185641050338745,lr : 0.001\n",
            "Epoch 29/50:  12%| | 70/606 [00:16<01:47,  4.97img2021-03-26 18:00:03,905 train.py[line:353] INFO: Train step_8520: loss : 24.987987518310547,loss xy : 15.95157241821289,loss wh : 1.1764118671417236,loss obj : 7.7129316329956055，loss cls : 0.14707207679748535,loss l2 : 4.885342597961426,lr : 0.001\n",
            "Epoch 29/50:  18%|▏| 110/606 [00:24<01:39,  5.01im2021-03-26 18:00:11,854 train.py[line:353] INFO: Train step_8540: loss : 15.145750045776367,loss xy : 5.799871921539307,loss wh : 0.630885124206543,loss obj : 8.659873008728027，loss cls : 0.055120084434747696,loss l2 : 3.449826240539551,lr : 0.001\n",
            "Epoch 29/50:  25%|▏| 150/606 [00:32<01:30,  5.04im2021-03-26 18:00:19,803 train.py[line:353] INFO: Train step_8560: loss : 3.0076005458831787,loss xy : 1.8242204189300537,loss wh : 0.004714047536253929,loss obj : 1.1735819578170776，loss cls : 0.005084285046905279,loss l2 : 0.04274415969848633,lr : 0.001\n",
            "Epoch 29/50:  31%|▎| 190/606 [00:40<01:23,  4.98im2021-03-26 18:00:27,760 train.py[line:353] INFO: Train step_8580: loss : 15.67282485961914,loss xy : 9.845881462097168,loss wh : 0.12756013870239258,loss obj : 4.009098529815674，loss cls : 1.690285086631775,loss l2 : 2.5429162979125977,lr : 0.001\n",
            "Epoch 29/50:  38%|▍| 230/606 [00:48<01:14,  5.08im2021-03-26 18:00:35,779 train.py[line:353] INFO: Train step_8600: loss : 37.74955749511719,loss xy : 21.714820861816406,loss wh : 0.47726312279701233,loss obj : 13.340309143066406，loss cls : 2.217163324356079,loss l2 : 5.020510673522949,lr : 0.001\n",
            "Epoch 29/50:  45%|▍| 270/606 [00:56<01:05,  5.15im2021-03-26 18:00:43,696 train.py[line:353] INFO: Train step_8620: loss : 41.26871109008789,loss xy : 17.150352478027344,loss wh : 2.143310546875,loss obj : 20.969987869262695，loss cls : 1.0050616264343262,loss l2 : 9.247611045837402,lr : 0.001\n",
            "Epoch 29/50:  51%|▌| 310/606 [01:04<00:58,  5.05im2021-03-26 18:00:51,622 train.py[line:353] INFO: Train step_8640: loss : 12.304131507873535,loss xy : 8.538228034973145,loss wh : 0.07600006461143494,loss obj : 3.437580108642578，loss cls : 0.2523232698440552,loss l2 : 1.1673846244812012,lr : 0.001\n",
            "Epoch 29/50:  58%|▌| 350/606 [01:12<00:50,  5.02im2021-03-26 18:00:59,558 train.py[line:353] INFO: Train step_8660: loss : 23.250961303710938,loss xy : 7.891105651855469,loss wh : 0.3354617953300476,loss obj : 9.375406265258789，loss cls : 5.648985862731934,loss l2 : 5.527796268463135,lr : 0.001\n",
            "Epoch 29/50:  64%|▋| 390/606 [01:19<00:42,  5.09im2021-03-26 18:01:07,491 train.py[line:353] INFO: Train step_8680: loss : 17.805320739746094,loss xy : 11.757467269897461,loss wh : 0.33082297444343567,loss obj : 5.133892059326172，loss cls : 0.5831373929977417,loss l2 : 2.154581308364868,lr : 0.001\n",
            "Epoch 29/50:  71%|▋| 430/606 [01:27<00:35,  4.99im2021-03-26 18:01:15,478 train.py[line:353] INFO: Train step_8700: loss : 19.514148712158203,loss xy : 13.296897888183594,loss wh : 0.4667792320251465,loss obj : 5.2274909019470215，loss cls : 0.5229799747467041,loss l2 : 2.6967992782592773,lr : 0.001\n",
            "Epoch 29/50:  78%|▊| 470/606 [01:35<00:27,  4.95im2021-03-26 18:01:23,466 train.py[line:353] INFO: Train step_8720: loss : 17.143522262573242,loss xy : 9.691492080688477,loss wh : 0.3601212799549103,loss obj : 6.494170665740967，loss cls : 0.5977388620376587,loss l2 : 3.121948719024658,lr : 0.001\n",
            "Epoch 29/50:  84%|▊| 510/606 [01:43<00:18,  5.13im2021-03-26 18:01:31,354 train.py[line:353] INFO: Train step_8740: loss : 18.915828704833984,loss xy : 8.831472396850586,loss wh : 0.7396942973136902,loss obj : 8.515341758728027，loss cls : 0.8293185234069824,loss l2 : 4.422165870666504,lr : 0.001\n",
            "Epoch 29/50:  91%|▉| 550/606 [01:51<00:11,  5.07im2021-03-26 18:01:39,250 train.py[line:353] INFO: Train step_8760: loss : 15.799455642700195,loss xy : 11.160036087036133,loss wh : 0.4063109755516052,loss obj : 4.042234420776367，loss cls : 0.1908741295337677,loss l2 : 2.212733507156372,lr : 0.001\n",
            "Epoch 29/50:  97%|▉| 590/606 [01:59<00:03,  5.19im2021-03-26 18:01:47,070 train.py[line:353] INFO: Train step_8780: loss : 20.57857894897461,loss xy : 13.890914916992188,loss wh : 0.2010490894317627,loss obj : 6.317509651184082，loss cls : 0.1691056787967682,loss l2 : 2.6081864833831787,lr : 0.001\n",
            "Epoch 29/50: 100%|█| 606/606 [02:02<00:00,  5.20im2021-03-26 18:01:50,297 train.py[line:364] INFO: Checkpoint 29 saved !\n",
            "Epoch 29/50: 100%|█| 606/606 [02:03<00:00,  4.92im\n",
            "Epoch 30/50:   4%| | 24/606 [00:07<02:08,  4.52img2021-03-26 18:01:58,046 train.py[line:353] INFO: Train step_8800: loss : 20.98824691772461,loss xy : 13.29526138305664,loss wh : 0.828342854976654,loss obj : 6.558778762817383，loss cls : 0.3058648109436035,loss l2 : 3.8801639080047607,lr : 0.001\n",
            "Epoch 30/50:  11%| | 64/606 [00:15<01:48,  5.02img2021-03-26 18:02:06,009 train.py[line:353] INFO: Train step_8820: loss : 24.828981399536133,loss xy : 18.58329963684082,loss wh : 0.3193829357624054,loss obj : 5.783786773681641，loss cls : 0.1425110548734665,loss l2 : 2.1143031120300293,lr : 0.001\n",
            "Epoch 30/50:  17%|▏| 104/606 [00:23<01:40,  4.99im2021-03-26 18:02:13,982 train.py[line:353] INFO: Train step_8840: loss : 13.260796546936035,loss xy : 8.719320297241211,loss wh : 0.866664707660675,loss obj : 3.622523307800293，loss cls : 0.052288129925727844,loss l2 : 2.887421131134033,lr : 0.001\n",
            "Epoch 30/50:  24%|▏| 144/606 [00:31<01:30,  5.08im2021-03-26 18:02:21,896 train.py[line:353] INFO: Train step_8860: loss : 7.037078380584717,loss xy : 3.6559858322143555,loss wh : 0.06087625399231911,loss obj : 3.311370372772217，loss cls : 0.008845603093504906,loss l2 : 1.0674943923950195,lr : 0.001\n",
            "Epoch 30/50:  30%|▎| 184/606 [00:39<01:24,  4.97im2021-03-26 18:02:29,886 train.py[line:353] INFO: Train step_8880: loss : 15.893909454345703,loss xy : 5.178232192993164,loss wh : 0.11347868293523788,loss obj : 10.601486206054688，loss cls : 0.0007119782967492938,loss l2 : 3.0004453659057617,lr : 0.001\n",
            "Epoch 30/50:  37%|▎| 224/606 [00:47<01:15,  5.06im2021-03-26 18:02:37,806 train.py[line:353] INFO: Train step_8900: loss : 8.178646087646484,loss xy : 5.756931304931641,loss wh : 0.17237886786460876,loss obj : 2.248267650604248，loss cls : 0.0010681243147701025,loss l2 : 0.8662805557250977,lr : 0.001\n",
            "Epoch 30/50:  44%|▍| 264/606 [00:55<01:07,  5.07im2021-03-26 18:02:45,779 train.py[line:353] INFO: Train step_8920: loss : 17.388669967651367,loss xy : 7.980494022369385,loss wh : 0.4387628436088562,loss obj : 8.874842643737793，loss cls : 0.09457068890333176,loss l2 : 2.9105989933013916,lr : 0.001\n",
            "Epoch 30/50:  50%|▌| 304/606 [01:03<00:59,  5.04im2021-03-26 18:02:53,733 train.py[line:353] INFO: Train step_8940: loss : 30.74875259399414,loss xy : 12.040151596069336,loss wh : 0.4387708306312561,loss obj : 12.77053165435791，loss cls : 5.499297142028809,loss l2 : 5.305181503295898,lr : 0.001\n",
            "Epoch 30/50:  57%|▌| 344/606 [01:10<00:52,  4.96im2021-03-26 18:03:01,684 train.py[line:353] INFO: Train step_8960: loss : 8.13572883605957,loss xy : 3.90653920173645,loss wh : 0.1475657820701599,loss obj : 4.066490173339844，loss cls : 0.015133430249989033,loss l2 : 1.402571201324463,lr : 0.001\n",
            "Epoch 30/50:  63%|▋| 384/606 [01:18<00:44,  5.00im2021-03-26 18:03:09,630 train.py[line:353] INFO: Train step_8980: loss : 13.950713157653809,loss xy : 6.789459228515625,loss wh : 0.12551230192184448,loss obj : 4.588448524475098，loss cls : 2.4472930431365967,loss l2 : 2.6531152725219727,lr : 0.001\n",
            "Epoch 30/50:  70%|▋| 424/606 [01:26<00:36,  5.04im2021-03-26 18:03:17,601 train.py[line:353] INFO: Train step_9000: loss : 22.431718826293945,loss xy : 14.389222145080566,loss wh : 0.73261559009552,loss obj : 7.102019786834717，loss cls : 0.2078610509634018,loss l2 : 3.4538846015930176,lr : 0.001\n",
            "Epoch 30/50:  77%|▊| 464/606 [01:34<00:28,  5.06im2021-03-26 18:03:25,511 train.py[line:353] INFO: Train step_9020: loss : 10.004986763000488,loss xy : 7.0633392333984375,loss wh : 0.18026554584503174,loss obj : 2.7271463871002197，loss cls : 0.03423582762479782,loss l2 : 1.2448689937591553,lr : 0.001\n",
            "Epoch 30/50:  83%|▊| 504/606 [01:42<00:20,  4.99im2021-03-26 18:03:33,480 train.py[line:353] INFO: Train step_9040: loss : 82.08723449707031,loss xy : 22.67645263671875,loss wh : 2.1422059535980225,loss obj : 50.466835021972656，loss cls : 6.801745414733887,loss l2 : 13.956941604614258,lr : 0.001\n",
            "Epoch 30/50:  90%|▉| 544/606 [01:50<00:12,  5.07im2021-03-26 18:03:41,414 train.py[line:353] INFO: Train step_9060: loss : 58.452056884765625,loss xy : 28.109821319580078,loss wh : 1.7191177606582642,loss obj : 26.369726181030273，loss cls : 2.253389835357666,loss l2 : 12.569807052612305,lr : 0.001\n",
            "Epoch 30/50:  96%|▉| 584/606 [01:58<00:04,  5.16im2021-03-26 18:03:49,293 train.py[line:353] INFO: Train step_9080: loss : 10.69522762298584,loss xy : 6.048501014709473,loss wh : 0.38893964886665344,loss obj : 4.254086017608643，loss cls : 0.003700756235048175,loss l2 : 1.6524370908737183,lr : 0.001\n",
            "Epoch 30/50: 100%|█| 606/606 [02:02<00:00,  5.19im2021-03-26 18:03:53,643 train.py[line:364] INFO: Checkpoint 30 saved !\n",
            "Epoch 30/50: 100%|█| 606/606 [02:03<00:00,  4.91im\n",
            "Epoch 31/50:   3%| | 18/606 [00:06<02:38,  3.70img2021-03-26 18:04:00,290 train.py[line:353] INFO: Train step_9100: loss : 19.78728485107422,loss xy : 12.112625122070312,loss wh : 1.1923518180847168,loss obj : 6.348804950714111，loss cls : 0.13350236415863037,loss l2 : 4.241670608520508,lr : 0.001\n",
            "Epoch 31/50:  10%| | 58/606 [00:14<01:49,  5.00img2021-03-26 18:04:08,301 train.py[line:353] INFO: Train step_9120: loss : 32.03724670410156,loss xy : 9.876786231994629,loss wh : 1.1407456398010254,loss obj : 20.01329803466797，loss cls : 1.0064173936843872,loss l2 : 6.754281044006348,lr : 0.001\n",
            "Epoch 31/50:  16%|▏| 98/606 [00:22<01:41,  5.00img2021-03-26 18:04:16,274 train.py[line:353] INFO: Train step_9140: loss : 27.514057159423828,loss xy : 15.460002899169922,loss wh : 1.0459158420562744,loss obj : 7.975744247436523，loss cls : 3.0323944091796875,loss l2 : 5.911865711212158,lr : 0.001\n",
            "Epoch 31/50:  23%|▏| 138/606 [00:30<01:32,  5.04im2021-03-26 18:04:24,199 train.py[line:353] INFO: Train step_9160: loss : 12.939480781555176,loss xy : 9.236997604370117,loss wh : 0.5012824535369873,loss obj : 2.8242197036743164，loss cls : 0.37698036432266235,loss l2 : 2.0345075130462646,lr : 0.001\n",
            "Epoch 31/50:  29%|▎| 178/606 [00:38<01:24,  5.05im2021-03-26 18:04:32,193 train.py[line:353] INFO: Train step_9180: loss : 28.870147705078125,loss xy : 14.400172233581543,loss wh : 0.35123249888420105,loss obj : 3.5484676361083984，loss cls : 10.570273399353027,loss l2 : 4.132693767547607,lr : 0.001\n",
            "Epoch 31/50:  36%|▎| 218/606 [00:46<01:16,  5.08im2021-03-26 18:04:40,101 train.py[line:353] INFO: Train step_9200: loss : 42.056678771972656,loss xy : 18.276615142822266,loss wh : 2.2972075939178467,loss obj : 18.945247650146484，loss cls : 2.5376086235046387,loss l2 : 10.351181030273438,lr : 0.001\n",
            "Epoch 31/50:  43%|▍| 258/606 [00:53<01:08,  5.05im2021-03-26 18:04:48,015 train.py[line:353] INFO: Train step_9220: loss : 53.534000396728516,loss xy : 17.63275909423828,loss wh : 1.5179845094680786,loss obj : 27.148052215576172，loss cls : 7.235203742980957,loss l2 : 10.628312110900879,lr : 0.001\n",
            "Epoch 31/50:  49%|▍| 298/606 [01:01<01:00,  5.08im2021-03-26 18:04:55,966 train.py[line:353] INFO: Train step_9240: loss : 8.471674919128418,loss xy : 5.408531665802002,loss wh : 0.04815993085503578,loss obj : 1.2547516822814941，loss cls : 1.760231614112854,loss l2 : 1.016310214996338,lr : 0.001\n",
            "Epoch 31/50:  56%|▌| 338/606 [01:09<00:52,  5.09im2021-03-26 18:05:03,925 train.py[line:353] INFO: Train step_9260: loss : 23.251609802246094,loss xy : 12.490506172180176,loss wh : 0.21801282465457916,loss obj : 9.817719459533691，loss cls : 0.7253719568252563,loss l2 : 3.9215362071990967,lr : 0.001\n",
            "Epoch 31/50:  62%|▌| 378/606 [01:17<00:45,  4.99im2021-03-26 18:05:11,902 train.py[line:353] INFO: Train step_9280: loss : 13.551583290100098,loss xy : 10.90962028503418,loss wh : 0.49327266216278076,loss obj : 0.7693718671798706，loss cls : 1.3793184757232666,loss l2 : 1.6012436151504517,lr : 0.001\n",
            "Epoch 31/50:  69%|▋| 418/606 [01:25<00:37,  5.00im2021-03-26 18:05:19,879 train.py[line:353] INFO: Train step_9300: loss : 39.3292121887207,loss xy : 17.16954231262207,loss wh : 1.0237928628921509,loss obj : 14.955307960510254，loss cls : 6.180569648742676,loss l2 : 7.023126125335693,lr : 0.001\n",
            "Epoch 31/50:  76%|▊| 458/606 [01:33<00:29,  4.98im2021-03-26 18:05:27,844 train.py[line:353] INFO: Train step_9320: loss : 15.57155704498291,loss xy : 10.628009796142578,loss wh : 0.347369521856308,loss obj : 4.565374374389648，loss cls : 0.03080330789089203,loss l2 : 2.077871561050415,lr : 0.001\n",
            "Epoch 31/50:  82%|▊| 498/606 [01:41<00:21,  5.08im2021-03-26 18:05:35,832 train.py[line:353] INFO: Train step_9340: loss : 6.492971420288086,loss xy : 5.682710647583008,loss wh : 0.06034822016954422,loss obj : 0.6993828415870667，loss cls : 0.05052993446588516,loss l2 : 0.3527962267398834,lr : 0.001\n",
            "Epoch 31/50:  89%|▉| 538/606 [01:49<00:13,  5.04im2021-03-26 18:05:43,722 train.py[line:353] INFO: Train step_9360: loss : 17.043794631958008,loss xy : 7.925544738769531,loss wh : 0.42461076378822327,loss obj : 7.915800094604492，loss cls : 0.7778376936912537,loss l2 : 3.024531126022339,lr : 0.001\n",
            "Epoch 31/50:  95%|▉| 578/606 [01:57<00:05,  5.14im2021-03-26 18:05:51,623 train.py[line:353] INFO: Train step_9380: loss : 23.092926025390625,loss xy : 11.47812557220459,loss wh : 1.011810064315796,loss obj : 10.358551025390625，loss cls : 0.2444387972354889,loss l2 : 4.378347396850586,lr : 0.001\n",
            "Epoch 31/50: 100%|█| 606/606 [02:02<00:00,  5.20im2021-03-26 18:05:57,163 train.py[line:364] INFO: Checkpoint 31 saved !\n",
            "Epoch 31/50: 100%|█| 606/606 [02:03<00:00,  4.91im\n",
            "Epoch 32/50:   2%| | 12/606 [00:04<03:57,  2.50img2021-03-26 18:06:02,410 train.py[line:353] INFO: Train step_9400: loss : 26.116069793701172,loss xy : 17.25124740600586,loss wh : 0.2061702460050583,loss obj : 8.339390754699707，loss cls : 0.3192594349384308,loss l2 : 3.286370277404785,lr : 0.001\n",
            "Epoch 32/50:   9%| | 52/606 [00:12<01:49,  5.05img2021-03-26 18:06:10,294 train.py[line:353] INFO: Train step_9420: loss : 20.68291664123535,loss xy : 11.469420433044434,loss wh : 0.5786483287811279,loss obj : 8.279491424560547，loss cls : 0.3553556799888611,loss l2 : 3.6013307571411133,lr : 0.001\n",
            "Epoch 32/50:  15%|▏| 92/606 [00:20<01:42,  4.99img2021-03-26 18:06:18,244 train.py[line:353] INFO: Train step_9440: loss : 16.39204216003418,loss xy : 12.970901489257812,loss wh : 0.5337383151054382,loss obj : 2.8587870597839355，loss cls : 0.028615424409508705,loss l2 : 1.6725690364837646,lr : 0.001\n",
            "Epoch 32/50:  22%|▏| 132/606 [00:28<01:34,  5.03im2021-03-26 18:06:26,226 train.py[line:353] INFO: Train step_9460: loss : 13.858028411865234,loss xy : 12.339627265930176,loss wh : 0.0987284854054451,loss obj : 1.2241852283477783，loss cls : 0.1954883337020874,loss l2 : 0.5125664472579956,lr : 0.001\n",
            "Epoch 32/50:  28%|▎| 172/606 [00:36<01:28,  4.91im2021-03-26 18:06:34,230 train.py[line:353] INFO: Train step_9480: loss : 11.252374649047852,loss xy : 5.506183624267578,loss wh : 0.2976739704608917,loss obj : 5.377640724182129，loss cls : 0.0708763375878334,loss l2 : 2.1778979301452637,lr : 0.001\n",
            "Epoch 32/50:  35%|▎| 212/606 [00:44<01:19,  4.94im2021-03-26 18:06:42,225 train.py[line:353] INFO: Train step_9500: loss : 27.28983497619629,loss xy : 12.372282028198242,loss wh : 0.28731873631477356,loss obj : 12.97116470336914，loss cls : 1.6590689420700073,loss l2 : 3.646286725997925,lr : 0.001\n",
            "Epoch 32/50:  42%|▍| 252/606 [00:52<01:10,  5.04im2021-03-26 18:06:50,205 train.py[line:353] INFO: Train step_9520: loss : 10.640618324279785,loss xy : 5.4134626388549805,loss wh : 0.28480666875839233,loss obj : 4.9321980476379395，loss cls : 0.01015110407024622,loss l2 : 1.9634993076324463,lr : 0.001\n",
            "Epoch 32/50:  48%|▍| 292/606 [01:00<01:02,  5.01im2021-03-26 18:06:58,226 train.py[line:353] INFO: Train step_9540: loss : 47.555755615234375,loss xy : 30.22393226623535,loss wh : 0.5692213177680969,loss obj : 15.04051685333252，loss cls : 1.7220839262008667,loss l2 : 6.893342971801758,lr : 0.001\n",
            "Epoch 32/50:  55%|▌| 332/606 [01:08<00:55,  4.98im2021-03-26 18:07:06,190 train.py[line:353] INFO: Train step_9560: loss : 41.075889587402344,loss xy : 24.87701988220215,loss wh : 1.2506402730941772,loss obj : 13.939440727233887，loss cls : 1.0087895393371582,loss l2 : 6.499805927276611,lr : 0.001\n",
            "Epoch 32/50:  61%|▌| 372/606 [01:16<00:46,  5.03im2021-03-26 18:07:14,171 train.py[line:353] INFO: Train step_9580: loss : 9.877236366271973,loss xy : 5.921134948730469,loss wh : 0.07724081724882126,loss obj : 3.8715357780456543，loss cls : 0.0073254285380244255,loss l2 : 1.2630903720855713,lr : 0.001\n",
            "Epoch 32/50:  68%|▋| 412/606 [01:24<00:38,  5.05im2021-03-26 18:07:22,125 train.py[line:353] INFO: Train step_9600: loss : 25.314306259155273,loss xy : 14.391077041625977,loss wh : 0.6050812005996704,loss obj : 9.868721961975098，loss cls : 0.44942623376846313,loss l2 : 3.8235442638397217,lr : 0.001\n",
            "Epoch 32/50:  75%|▋| 452/606 [01:32<00:30,  5.07im2021-03-26 18:07:30,079 train.py[line:353] INFO: Train step_9620: loss : 19.003984451293945,loss xy : 14.290658950805664,loss wh : 0.25656113028526306,loss obj : 3.7798569202423096，loss cls : 0.6769062280654907,loss l2 : 1.9583543539047241,lr : 0.001\n",
            "Epoch 32/50:  81%|▊| 492/606 [01:40<00:22,  5.04im2021-03-26 18:07:38,027 train.py[line:353] INFO: Train step_9640: loss : 8.510958671569824,loss xy : 5.051872730255127,loss wh : 0.05412471666932106,loss obj : 3.398062229156494，loss cls : 0.006899071857333183,loss l2 : 0.9495107531547546,lr : 0.001\n",
            "Epoch 32/50:  88%|▉| 532/606 [01:48<00:15,  4.93im2021-03-26 18:07:46,069 train.py[line:353] INFO: Train step_9660: loss : 28.333662033081055,loss xy : 12.381830215454102,loss wh : 0.8072017431259155,loss obj : 8.996712684631348，loss cls : 6.147916316986084,loss l2 : 6.555607318878174,lr : 0.001\n",
            "Epoch 32/50:  94%|▉| 572/606 [01:56<00:06,  5.11im2021-03-26 18:07:54,062 train.py[line:353] INFO: Train step_9680: loss : 49.226863861083984,loss xy : 23.285810470581055,loss wh : 1.2864508628845215,loss obj : 17.80517578125，loss cls : 6.849427223205566,loss l2 : 9.462024688720703,lr : 0.001\n",
            "Epoch 32/50: 100%|█| 606/606 [02:03<00:00,  5.21im2021-03-26 18:08:00,778 train.py[line:364] INFO: Checkpoint 32 saved !\n",
            "Epoch 32/50: 100%|█| 606/606 [02:03<00:00,  4.90im\n",
            "Epoch 33/50:   1%| | 6/606 [00:03<07:16,  1.37img/2021-03-26 18:08:04,588 train.py[line:353] INFO: Train step_9700: loss : 13.017708778381348,loss xy : 7.232427597045898,loss wh : 0.2777281701564789,loss obj : 4.986080646514893，loss cls : 0.521472156047821,loss l2 : 2.1506757736206055,lr : 0.001\n",
            "Epoch 33/50:   8%| | 46/606 [00:11<01:52,  5.00img2021-03-26 18:08:12,496 train.py[line:353] INFO: Train step_9720: loss : 12.537124633789062,loss xy : 7.458705902099609,loss wh : 0.2930121421813965,loss obj : 4.380061149597168，loss cls : 0.40534621477127075,loss l2 : 2.074263572692871,lr : 0.001\n",
            "Epoch 33/50:  14%|▏| 86/606 [00:19<01:41,  5.10img2021-03-26 18:08:20,340 train.py[line:353] INFO: Train step_9740: loss : 10.605485916137695,loss xy : 6.555509567260742,loss wh : 0.43225669860839844,loss obj : 3.588852643966675，loss cls : 0.02886715717613697,loss l2 : 1.961425542831421,lr : 0.001\n",
            "Epoch 33/50:  21%|▏| 126/606 [00:27<01:36,  4.97im2021-03-26 18:08:28,295 train.py[line:353] INFO: Train step_9760: loss : 44.861412048339844,loss xy : 16.574487686157227,loss wh : 1.8203673362731934,loss obj : 25.106189727783203，loss cls : 1.360368251800537,loss l2 : 9.573982238769531,lr : 0.001\n",
            "Epoch 33/50:  27%|▎| 166/606 [00:35<01:26,  5.08im2021-03-26 18:08:36,280 train.py[line:353] INFO: Train step_9780: loss : 14.740133285522461,loss xy : 3.8465466499328613,loss wh : 0.016728516668081284,loss obj : 10.8494234085083，loss cls : 0.02743501029908657,loss l2 : 3.3460464477539062,lr : 0.001\n",
            "Epoch 33/50:  34%|▎| 206/606 [00:42<01:17,  5.14im2021-03-26 18:08:44,157 train.py[line:353] INFO: Train step_9800: loss : 28.42190933227539,loss xy : 16.868412017822266,loss wh : 1.065451741218567,loss obj : 9.940671920776367，loss cls : 0.5473729968070984,loss l2 : 5.086668014526367,lr : 0.001\n",
            "Epoch 33/50:  41%|▍| 246/606 [00:50<01:10,  5.11im2021-03-26 18:08:52,023 train.py[line:353] INFO: Train step_9820: loss : 18.26915168762207,loss xy : 14.031244277954102,loss wh : 0.7400567531585693,loss obj : 3.371873617172241，loss cls : 0.12597711384296417,loss l2 : 2.6063361167907715,lr : 0.001\n",
            "Epoch 33/50:  47%|▍| 286/606 [00:58<01:04,  4.98im2021-03-26 18:09:00,020 train.py[line:353] INFO: Train step_9840: loss : 16.099956512451172,loss xy : 6.954092979431152,loss wh : 0.0842963382601738,loss obj : 7.284459590911865，loss cls : 1.7771066427230835,loss l2 : 3.2735307216644287,lr : 0.001\n",
            "Epoch 33/50:  54%|▌| 326/606 [01:06<00:56,  4.97im2021-03-26 18:09:07,965 train.py[line:353] INFO: Train step_9860: loss : 28.204715728759766,loss xy : 19.099422454833984,loss wh : 0.5097854137420654,loss obj : 7.8396220207214355，loss cls : 0.7558855414390564,loss l2 : 3.5741922855377197,lr : 0.001\n",
            "Epoch 33/50:  60%|▌| 366/606 [01:14<00:47,  5.08im2021-03-26 18:09:15,911 train.py[line:353] INFO: Train step_9880: loss : 15.007551193237305,loss xy : 5.257874965667725,loss wh : 0.5426031947135925,loss obj : 9.206162452697754，loss cls : 0.000910372007638216,loss l2 : 3.326072931289673,lr : 0.001\n",
            "Epoch 33/50:  67%|▋| 406/606 [01:22<00:39,  5.04im2021-03-26 18:09:23,773 train.py[line:353] INFO: Train step_9900: loss : 9.079861640930176,loss xy : 7.61681604385376,loss wh : 0.15716026723384857,loss obj : 1.2981334924697876，loss cls : 0.00775191793218255,loss l2 : 0.811977207660675,lr : 0.001\n",
            "Epoch 33/50:  74%|▋| 446/606 [01:30<00:31,  5.05im2021-03-26 18:09:31,632 train.py[line:353] INFO: Train step_9920: loss : 30.866878509521484,loss xy : 20.688095092773438,loss wh : 0.5248236656188965,loss obj : 9.371317863464355，loss cls : 0.2826416492462158,loss l2 : 4.242184162139893,lr : 0.001\n",
            "Epoch 33/50:  80%|▊| 486/606 [01:38<00:23,  5.03im2021-03-26 18:09:39,567 train.py[line:353] INFO: Train step_9940: loss : 11.456466674804688,loss xy : 8.646150588989258,loss wh : 0.22433686256408691,loss obj : 2.515183925628662，loss cls : 0.07079508900642395,loss l2 : 1.2132245302200317,lr : 0.001\n",
            "Epoch 33/50:  87%|▊| 526/606 [01:46<00:15,  5.02im2021-03-26 18:09:47,520 train.py[line:353] INFO: Train step_9960: loss : 25.789770126342773,loss xy : 9.223691940307617,loss wh : 0.3371230363845825,loss obj : 14.149025917053223，loss cls : 2.0799295902252197,loss l2 : 4.9637274742126465,lr : 0.001\n",
            "Epoch 33/50:  93%|▉| 566/606 [01:54<00:07,  5.04im2021-03-26 18:09:55,455 train.py[line:353] INFO: Train step_9980: loss : 31.393878936767578,loss xy : 8.390413284301758,loss wh : 0.53492271900177,loss obj : 8.772068977355957，loss cls : 13.696473121643066,loss l2 : 6.0201416015625,lr : 0.001\n",
            "Epoch 33/50: 100%|█| 606/606 [02:02<00:00,  5.22im2021-03-26 18:10:03,302 train.py[line:364] INFO: Checkpoint 33 saved !\n",
            "Epoch 33/50: 100%|█| 606/606 [02:02<00:00,  4.95im\n",
            "Epoch 34/50:   0%|       | 0/606 [00:00<?, ?img/s]2021-03-26 18:10:05,890 train.py[line:353] INFO: Train step_10000: loss : 12.735225677490234,loss xy : 8.616830825805664,loss wh : 0.34664833545684814,loss obj : 3.7582647800445557，loss cls : 0.013482021167874336,loss l2 : 1.749943733215332,lr : 0.001\n",
            "Epoch 34/50:   7%| | 40/606 [00:10<01:52,  5.02img2021-03-26 18:10:14,071 train.py[line:353] INFO: Train step_10020: loss : 24.234222412109375,loss xy : 11.467218399047852,loss wh : 0.3328109383583069,loss obj : 11.737576484680176，loss cls : 0.6966175436973572,loss l2 : 3.9113059043884277,lr : 0.001\n",
            "Epoch 34/50:  13%|▏| 80/606 [00:18<01:45,  4.98img2021-03-26 18:10:22,081 train.py[line:353] INFO: Train step_10040: loss : 28.06484603881836,loss xy : 20.365224838256836,loss wh : 0.6764624714851379,loss obj : 6.844982624053955，loss cls : 0.1781773716211319,loss l2 : 3.8706936836242676,lr : 0.001\n",
            "Epoch 34/50:  20%|▏| 120/606 [00:26<01:35,  5.08im2021-03-26 18:10:30,041 train.py[line:353] INFO: Train step_10060: loss : 38.156612396240234,loss xy : 23.466150283813477,loss wh : 0.9161935448646545,loss obj : 12.283524513244629，loss cls : 1.4907463788986206,loss l2 : 6.179378986358643,lr : 0.001\n",
            "Epoch 34/50:  26%|▎| 160/606 [00:34<01:27,  5.07im2021-03-26 18:10:37,997 train.py[line:353] INFO: Train step_10080: loss : 29.54906463623047,loss xy : 15.952340126037598,loss wh : 0.25289061665534973,loss obj : 5.275257587432861，loss cls : 8.06857681274414,loss l2 : 5.560985088348389,lr : 0.001\n",
            "Epoch 34/50:  33%|▎| 200/606 [00:42<01:20,  5.02im2021-03-26 18:10:45,945 train.py[line:353] INFO: Train step_10100: loss : 24.529266357421875,loss xy : 18.98626708984375,loss wh : 0.44966208934783936,loss obj : 4.848659038543701，loss cls : 0.24467848241329193,loss l2 : 2.675213575363159,lr : 0.001\n",
            "Epoch 34/50:  40%|▍| 240/606 [00:50<01:13,  4.99im2021-03-26 18:10:53,966 train.py[line:353] INFO: Train step_10120: loss : 18.812511444091797,loss xy : 7.432522296905518,loss wh : 0.18795712292194366,loss obj : 11.07692813873291，loss cls : 0.11510501801967621,loss l2 : 3.6069350242614746,lr : 0.001\n",
            "Epoch 34/50:  46%|▍| 280/606 [00:58<01:05,  5.01im2021-03-26 18:11:01,893 train.py[line:353] INFO: Train step_10140: loss : 12.026878356933594,loss xy : 9.723586082458496,loss wh : 0.13442644476890564,loss obj : 2.15433931350708，loss cls : 0.014526613987982273,loss l2 : 1.288246512413025,lr : 0.001\n",
            "Epoch 34/50:  53%|▌| 320/606 [01:06<00:57,  5.01im2021-03-26 18:11:09,897 train.py[line:353] INFO: Train step_10160: loss : 30.284698486328125,loss xy : 11.8082275390625,loss wh : 1.232272982597351,loss obj : 15.914976119995117，loss cls : 1.3292219638824463,loss l2 : 5.458240509033203,lr : 0.001\n",
            "Epoch 34/50:  59%|▌| 360/606 [01:14<00:48,  5.11im2021-03-26 18:11:17,875 train.py[line:353] INFO: Train step_10180: loss : 15.785051345825195,loss xy : 12.410602569580078,loss wh : 0.15858973562717438,loss obj : 3.1747944355010986，loss cls : 0.04106557369232178,loss l2 : 0.7925881743431091,lr : 0.001\n",
            "Epoch 34/50:  66%|▋| 400/606 [01:22<00:40,  5.05im2021-03-26 18:11:25,765 train.py[line:353] INFO: Train step_10200: loss : 16.716632843017578,loss xy : 10.669451713562012,loss wh : 0.18514734506607056,loss obj : 5.145937442779541，loss cls : 0.716097354888916,loss l2 : 2.1894125938415527,lr : 0.001\n",
            "Epoch 34/50:  73%|▋| 440/606 [01:30<00:32,  5.06im2021-03-26 18:11:33,734 train.py[line:353] INFO: Train step_10220: loss : 10.072294235229492,loss xy : 7.4969635009765625,loss wh : 0.19961458444595337,loss obj : 2.2499516010284424，loss cls : 0.12576454877853394,loss l2 : 0.9354877471923828,lr : 0.001\n",
            "Epoch 34/50:  79%|▊| 480/606 [01:37<00:24,  5.05im2021-03-26 18:11:41,692 train.py[line:353] INFO: Train step_10240: loss : 10.393787384033203,loss xy : 8.444664001464844,loss wh : 0.1789918690919876,loss obj : 1.762762188911438，loss cls : 0.0073691303841769695,loss l2 : 1.0449941158294678,lr : 0.001\n",
            "Epoch 34/50:  86%|▊| 520/606 [01:45<00:17,  5.01im2021-03-26 18:11:49,647 train.py[line:353] INFO: Train step_10260: loss : 24.081159591674805,loss xy : 19.014684677124023,loss wh : 0.42635664343833923,loss obj : 4.608334541320801，loss cls : 0.03178270161151886,loss l2 : 2.142564058303833,lr : 0.001\n",
            "Epoch 34/50:  92%|▉| 560/606 [01:53<00:09,  5.00im2021-03-26 18:11:57,673 train.py[line:353] INFO: Train step_10280: loss : 22.113935470581055,loss xy : 9.16431999206543,loss wh : 0.14873261749744415,loss obj : 10.028524398803711，loss cls : 2.772359848022461,loss l2 : 3.69549298286438,lr : 0.001\n",
            "Epoch 34/50:  99%|▉| 600/606 [02:01<00:01,  5.19im2021-03-26 18:12:05,460 train.py[line:353] INFO: Train step_10300: loss : 5.7432427406311035,loss xy : 4.291022777557373,loss wh : 0.1299830824136734,loss obj : 1.1284515857696533，loss cls : 0.19378520548343658,loss l2 : 0.4125311076641083,lr : 0.001\n",
            "Epoch 34/50: 100%|█| 606/606 [02:02<00:00,  5.18im2021-03-26 18:12:06,750 train.py[line:364] INFO: Checkpoint 34 saved !\n",
            "Epoch 34/50: 100%|█| 606/606 [02:03<00:00,  4.91im\n",
            "Epoch 35/50:   6%| | 34/606 [00:08<01:55,  4.96img2021-03-26 18:12:16,008 train.py[line:353] INFO: Train step_10320: loss : 20.826692581176758,loss xy : 12.866541862487793,loss wh : 0.7765407562255859,loss obj : 7.115682601928711，loss cls : 0.06792563199996948,loss l2 : 3.844874382019043,lr : 0.001\n",
            "Epoch 35/50:  12%| | 74/606 [00:16<01:46,  5.00img2021-03-26 18:12:23,987 train.py[line:353] INFO: Train step_10340: loss : 22.14405059814453,loss xy : 10.641687393188477,loss wh : 0.5157617926597595,loss obj : 10.46396255493164，loss cls : 0.5226398706436157,loss l2 : 3.912015438079834,lr : 0.001\n",
            "Epoch 35/50:  19%|▏| 114/606 [00:24<01:39,  4.95im2021-03-26 18:12:32,001 train.py[line:353] INFO: Train step_10360: loss : 36.05467987060547,loss xy : 15.501116752624512,loss wh : 0.6595474481582642,loss obj : 14.202836990356445，loss cls : 5.691178321838379,loss l2 : 5.9543986320495605,lr : 0.001\n",
            "Epoch 35/50:  25%|▎| 154/606 [00:32<01:28,  5.11im2021-03-26 18:12:39,886 train.py[line:353] INFO: Train step_10380: loss : 12.618180274963379,loss xy : 9.292154312133789,loss wh : 0.16388966143131256,loss obj : 3.1579716205596924，loss cls : 0.004165151156485081,loss l2 : 1.3897570371627808,lr : 0.001\n",
            "Epoch 35/50:  32%|▎| 194/606 [00:40<01:21,  5.07im2021-03-26 18:12:47,876 train.py[line:353] INFO: Train step_10400: loss : 8.725494384765625,loss xy : 6.584521293640137,loss wh : 0.07510987669229507,loss obj : 2.0065324306488037，loss cls : 0.05933067202568054,loss l2 : 0.6186224818229675,lr : 0.001\n",
            "Epoch 35/50:  39%|▍| 234/606 [00:48<01:17,  4.83im2021-03-26 18:12:55,944 train.py[line:353] INFO: Train step_10420: loss : 30.49460792541504,loss xy : 11.81479549407959,loss wh : 0.6907294988632202,loss obj : 17.678709030151367，loss cls : 0.3103736340999603,loss l2 : 5.080414772033691,lr : 0.001\n",
            "Epoch 35/50:  45%|▍| 274/606 [00:56<01:06,  4.97im2021-03-26 18:13:03,890 train.py[line:353] INFO: Train step_10440: loss : 12.990060806274414,loss xy : 9.4414644241333,loss wh : 0.48053932189941406,loss obj : 2.9434823989868164，loss cls : 0.12457454949617386,loss l2 : 1.7827379703521729,lr : 0.001\n",
            "Epoch 35/50:  52%|▌| 314/606 [01:04<00:58,  5.03im2021-03-26 18:13:11,807 train.py[line:353] INFO: Train step_10460: loss : 32.766849517822266,loss xy : 17.093395233154297,loss wh : 0.891707181930542,loss obj : 9.145553588867188，loss cls : 5.636193752288818,loss l2 : 6.340343952178955,lr : 0.001\n",
            "Epoch 35/50:  58%|▌| 354/606 [01:12<00:51,  4.93im2021-03-26 18:13:19,743 train.py[line:353] INFO: Train step_10480: loss : 10.827439308166504,loss xy : 7.06147575378418,loss wh : 0.18416139483451843,loss obj : 3.4015088081359863，loss cls : 0.1802930235862732,loss l2 : 1.6768498420715332,lr : 0.001\n",
            "Epoch 35/50:  65%|▋| 394/606 [01:20<00:42,  5.04im2021-03-26 18:13:27,660 train.py[line:353] INFO: Train step_10500: loss : 12.502365112304688,loss xy : 10.989463806152344,loss wh : 0.07618583738803864,loss obj : 1.3626673221588135，loss cls : 0.07404840737581253,loss l2 : 0.539890706539154,lr : 0.001\n",
            "Epoch 35/50:  72%|▋| 434/606 [01:28<00:34,  5.02im2021-03-26 18:13:35,630 train.py[line:353] INFO: Train step_10520: loss : 24.831729888916016,loss xy : 4.423222064971924,loss wh : 0.12347458302974701,loss obj : 17.11099624633789，loss cls : 3.174038887023926,loss l2 : 6.699286937713623,lr : 0.001\n",
            "Epoch 35/50:  78%|▊| 474/606 [01:36<00:26,  4.93im2021-03-26 18:13:43,596 train.py[line:353] INFO: Train step_10540: loss : 34.91489028930664,loss xy : 24.79060173034668,loss wh : 0.9526291489601135,loss obj : 9.06131362915039，loss cls : 0.11034901440143585,loss l2 : 5.016097068786621,lr : 0.001\n",
            "Epoch 35/50:  85%|▊| 514/606 [01:44<00:18,  5.09im2021-03-26 18:13:51,508 train.py[line:353] INFO: Train step_10560: loss : 39.44013977050781,loss xy : 18.884431838989258,loss wh : 1.5974832773208618,loss obj : 17.00228500366211，loss cls : 1.9559383392333984,loss l2 : 6.842399597167969,lr : 0.001\n",
            "Epoch 35/50:  91%|▉| 554/606 [01:52<00:10,  5.05im2021-03-26 18:13:59,467 train.py[line:353] INFO: Train step_10580: loss : 36.813812255859375,loss xy : 14.106245040893555,loss wh : 0.4131377041339874,loss obj : 16.82181739807129，loss cls : 5.472610950469971,loss l2 : 6.969179153442383,lr : 0.001\n",
            "Epoch 35/50:  98%|▉| 594/606 [02:00<00:02,  5.20im2021-03-26 18:14:07,315 train.py[line:353] INFO: Train step_10600: loss : 6.394468307495117,loss xy : 4.773839950561523,loss wh : 0.16525647044181824,loss obj : 1.3221222162246704，loss cls : 0.13324998319149017,loss l2 : 0.7242176532745361,lr : 0.001\n",
            "Epoch 35/50: 100%|█| 606/606 [02:02<00:00,  5.21im2021-03-26 18:14:09,771 train.py[line:364] INFO: Checkpoint 35 saved !\n",
            "Epoch 35/50: 100%|█| 606/606 [02:03<00:00,  4.93im\n",
            "Epoch 36/50:   5%| | 28/606 [00:08<02:01,  4.75img2021-03-26 18:14:18,295 train.py[line:353] INFO: Train step_10620: loss : 17.047910690307617,loss xy : 6.607306480407715,loss wh : 0.08750103414058685,loss obj : 10.325119018554688，loss cls : 0.027985045686364174,loss l2 : 3.0878195762634277,lr : 0.001\n",
            "Epoch 36/50:  11%| | 68/606 [00:16<01:47,  5.02img2021-03-26 18:14:26,240 train.py[line:353] INFO: Train step_10640: loss : 50.10526657104492,loss xy : 24.04327392578125,loss wh : 0.8192568421363831,loss obj : 23.482301712036133，loss cls : 1.760433554649353,loss l2 : 7.258407115936279,lr : 0.001\n",
            "Epoch 36/50:  18%|▏| 108/606 [00:24<01:40,  4.93im2021-03-26 18:14:34,218 train.py[line:353] INFO: Train step_10660: loss : 39.906917572021484,loss xy : 17.046756744384766,loss wh : 0.8307052850723267,loss obj : 17.26121711730957，loss cls : 4.768237590789795,loss l2 : 8.552184104919434,lr : 0.001\n",
            "Epoch 36/50:  24%|▏| 148/606 [00:32<01:30,  5.04im2021-03-26 18:14:42,210 train.py[line:353] INFO: Train step_10680: loss : 12.650941848754883,loss xy : 9.24782943725586,loss wh : 0.12510386109352112,loss obj : 3.2454025745391846，loss cls : 0.03260623291134834,loss l2 : 1.2319917678833008,lr : 0.001\n",
            "Epoch 36/50:  31%|▎| 188/606 [00:40<01:24,  4.96im2021-03-26 18:14:50,248 train.py[line:353] INFO: Train step_10700: loss : 29.56572723388672,loss xy : 11.122983932495117,loss wh : 5.092288017272949,loss obj : 10.286559104919434，loss cls : 3.063897132873535,loss l2 : 13.517572402954102,lr : 0.001\n",
            "Epoch 36/50:  38%|▍| 228/606 [00:48<01:16,  4.95im2021-03-26 18:14:58,272 train.py[line:353] INFO: Train step_10720: loss : 27.061161041259766,loss xy : 15.844865798950195,loss wh : 0.4545174241065979,loss obj : 8.895407676696777，loss cls : 1.866371750831604,loss l2 : 4.503841876983643,lr : 0.001\n",
            "Epoch 36/50:  44%|▍| 268/606 [00:56<01:06,  5.05im2021-03-26 18:15:06,250 train.py[line:353] INFO: Train step_10740: loss : 32.0816764831543,loss xy : 7.510590553283691,loss wh : 0.6124863624572754,loss obj : 16.733417510986328，loss cls : 7.2251811027526855,loss l2 : 7.500228404998779,lr : 0.001\n",
            "Epoch 36/50:  51%|▌| 308/606 [01:04<01:00,  4.93im2021-03-26 18:15:14,291 train.py[line:353] INFO: Train step_10760: loss : 29.023273468017578,loss xy : 14.366233825683594,loss wh : 0.8895550966262817,loss obj : 13.726165771484375，loss cls : 0.04131898283958435,loss l2 : 6.978776454925537,lr : 0.001\n",
            "Epoch 36/50:  57%|▌| 348/606 [01:12<00:51,  5.03im2021-03-26 18:15:22,191 train.py[line:353] INFO: Train step_10780: loss : 13.574000358581543,loss xy : 7.89250373840332,loss wh : 0.34528279304504395,loss obj : 5.248901844024658，loss cls : 0.08731159567832947,loss l2 : 2.422555446624756,lr : 0.001\n",
            "Epoch 36/50:  64%|▋| 388/606 [01:19<00:43,  5.06im2021-03-26 18:15:30,063 train.py[line:353] INFO: Train step_10800: loss : 23.354808807373047,loss xy : 14.022199630737305,loss wh : 0.7720081806182861,loss obj : 7.633635520935059，loss cls : 0.9269649982452393,loss l2 : 3.732731819152832,lr : 0.001\n",
            "Epoch 36/50:  71%|▋| 428/606 [01:27<00:35,  5.06im2021-03-26 18:15:38,041 train.py[line:353] INFO: Train step_10820: loss : 7.298343181610107,loss xy : 4.013141632080078,loss wh : 0.03589659556746483,loss obj : 3.2491979598999023，loss cls : 0.00010693212243495509,loss l2 : 1.070520281791687,lr : 0.001\n",
            "Epoch 36/50:  77%|▊| 468/606 [01:35<00:27,  5.00im2021-03-26 18:15:45,978 train.py[line:353] INFO: Train step_10840: loss : 77.28071594238281,loss xy : 33.72163009643555,loss wh : 2.009549856185913,loss obj : 34.1863899230957，loss cls : 7.36314058303833,loss l2 : 14.77991008758545,lr : 0.001\n",
            "Epoch 36/50:  84%|▊| 508/606 [01:43<00:19,  5.08im2021-03-26 18:15:53,897 train.py[line:353] INFO: Train step_10860: loss : 45.986331939697266,loss xy : 23.206100463867188,loss wh : 0.7295656800270081,loss obj : 21.796058654785156，loss cls : 0.25460413098335266,loss l2 : 8.222764015197754,lr : 0.001\n",
            "Epoch 36/50:  90%|▉| 548/606 [01:51<00:11,  5.01im2021-03-26 18:16:01,865 train.py[line:353] INFO: Train step_10880: loss : 21.297077178955078,loss xy : 16.649967193603516,loss wh : 0.6326614022254944,loss obj : 3.5228240489959717，loss cls : 0.4916245937347412,loss l2 : 2.4081268310546875,lr : 0.001\n",
            "Epoch 36/50:  97%|▉| 588/606 [01:59<00:03,  5.18im2021-03-26 18:16:09,731 train.py[line:353] INFO: Train step_10900: loss : 24.149274826049805,loss xy : 16.7208251953125,loss wh : 1.114427089691162,loss obj : 5.967897891998291，loss cls : 0.3461246192455292,loss l2 : 4.050943374633789,lr : 0.001\n",
            "Epoch 36/50: 100%|█| 606/606 [02:03<00:00,  5.19im2021-03-26 18:16:13,357 train.py[line:364] INFO: Checkpoint 36 saved !\n",
            "Epoch 36/50: 100%|█| 606/606 [02:03<00:00,  4.90im\n",
            "Epoch 37/50:   4%| | 22/606 [00:06<02:14,  4.33img2021-03-26 18:16:20,763 train.py[line:353] INFO: Train step_10920: loss : 15.873167037963867,loss xy : 11.295331001281738,loss wh : 0.3868038058280945,loss obj : 4.14103364944458，loss cls : 0.049999162554740906,loss l2 : 1.6491589546203613,lr : 0.001\n",
            "Epoch 37/50:  10%| | 62/606 [00:14<01:48,  5.02img2021-03-26 18:16:28,717 train.py[line:353] INFO: Train step_10940: loss : 17.690271377563477,loss xy : 13.249749183654785,loss wh : 0.26920217275619507,loss obj : 4.108328342437744，loss cls : 0.06299247592687607,loss l2 : 2.1281192302703857,lr : 0.001\n",
            "Epoch 37/50:  17%|▏| 102/606 [00:22<01:40,  5.01im2021-03-26 18:16:36,674 train.py[line:353] INFO: Train step_10960: loss : 15.766201972961426,loss xy : 11.017303466796875,loss wh : 0.24083176255226135,loss obj : 4.361911773681641，loss cls : 0.14615580439567566,loss l2 : 2.113940477371216,lr : 0.001\n",
            "Epoch 37/50:  23%|▏| 142/606 [00:30<01:33,  4.95im2021-03-26 18:16:44,647 train.py[line:353] INFO: Train step_10980: loss : 13.725149154663086,loss xy : 3.658452033996582,loss wh : 0.1665971428155899,loss obj : 9.499698638916016，loss cls : 0.40040087699890137,loss l2 : 1.9145615100860596,lr : 0.001\n",
            "Epoch 37/50:  30%|▎| 182/606 [00:38<01:24,  5.03im2021-03-26 18:16:52,627 train.py[line:353] INFO: Train step_11000: loss : 31.966285705566406,loss xy : 17.46631622314453,loss wh : 0.497236430644989,loss obj : 12.094627380371094，loss cls : 1.908105731010437,loss l2 : 5.447101593017578,lr : 0.001\n",
            "Epoch 37/50:  37%|▎| 222/606 [00:46<01:16,  5.01im2021-03-26 18:17:00,607 train.py[line:353] INFO: Train step_11020: loss : 16.22903060913086,loss xy : 11.731521606445312,loss wh : 0.3989933431148529,loss obj : 3.6713192462921143，loss cls : 0.42719709873199463,loss l2 : 2.3381123542785645,lr : 0.001\n",
            "Epoch 37/50:  43%|▍| 262/606 [00:54<01:08,  4.99im2021-03-26 18:17:08,569 train.py[line:353] INFO: Train step_11040: loss : 19.843908309936523,loss xy : 14.708059310913086,loss wh : 0.4727939963340759,loss obj : 2.634134292602539，loss cls : 2.0289204120635986,loss l2 : 2.610454559326172,lr : 0.001\n",
            "Epoch 37/50:  50%|▍| 302/606 [01:02<01:01,  4.98im2021-03-26 18:17:16,542 train.py[line:353] INFO: Train step_11060: loss : 9.492512702941895,loss xy : 5.838992595672607,loss wh : 0.09960880130529404,loss obj : 3.553361177444458，loss cls : 0.0005502794519998133,loss l2 : 1.0767171382904053,lr : 0.001\n",
            "Epoch 37/50:  56%|▌| 342/606 [01:10<00:51,  5.12im2021-03-26 18:17:24,466 train.py[line:353] INFO: Train step_11080: loss : 12.828715324401855,loss xy : 5.5042500495910645,loss wh : 0.11738608777523041,loss obj : 7.011893272399902，loss cls : 0.1951855719089508,loss l2 : 2.3805370330810547,lr : 0.001\n",
            "Epoch 37/50:  63%|▋| 382/606 [01:18<00:44,  5.03im2021-03-26 18:17:32,454 train.py[line:353] INFO: Train step_11100: loss : 25.60822296142578,loss xy : 17.462060928344727,loss wh : 1.4693937301635742,loss obj : 6.476473331451416，loss cls : 0.20029544830322266,loss l2 : 4.890926837921143,lr : 0.001\n",
            "Epoch 37/50:  70%|▋| 422/606 [01:26<00:36,  5.04im2021-03-26 18:17:40,412 train.py[line:353] INFO: Train step_11120: loss : 14.533648490905762,loss xy : 7.8310418128967285,loss wh : 0.3191376030445099,loss obj : 6.26200008392334，loss cls : 0.12146887928247452,loss l2 : 2.5997965335845947,lr : 0.001\n",
            "Epoch 37/50:  76%|▊| 462/606 [01:34<00:28,  5.13im2021-03-26 18:17:48,275 train.py[line:353] INFO: Train step_11140: loss : 20.552642822265625,loss xy : 8.025100708007812,loss wh : 0.5235127806663513,loss obj : 10.08905029296875，loss cls : 1.9149783849716187,loss l2 : 3.4071671962738037,lr : 0.001\n",
            "Epoch 37/50:  83%|▊| 502/606 [01:42<00:20,  5.06im2021-03-26 18:17:56,183 train.py[line:353] INFO: Train step_11160: loss : 45.887123107910156,loss xy : 24.429210662841797,loss wh : 0.9278603792190552,loss obj : 19.37807846069336，loss cls : 1.151975393295288,loss l2 : 7.3037261962890625,lr : 0.001\n",
            "Epoch 37/50:  89%|▉| 542/606 [01:50<00:12,  4.99im2021-03-26 18:18:04,135 train.py[line:353] INFO: Train step_11180: loss : 39.32394027709961,loss xy : 18.87428855895996,loss wh : 0.7664808630943298,loss obj : 14.14665699005127，loss cls : 5.5365142822265625,loss l2 : 6.247959613800049,lr : 0.001\n",
            "Epoch 37/50:  96%|▉| 582/606 [01:58<00:04,  5.15im2021-03-26 18:18:12,065 train.py[line:353] INFO: Train step_11200: loss : 28.02831268310547,loss xy : 15.864117622375488,loss wh : 0.4164653420448303,loss obj : 10.657244682312012，loss cls : 1.0904850959777832,loss l2 : 4.61797571182251,lr : 0.001\n",
            "Epoch 37/50: 100%|█| 606/606 [02:02<00:00,  5.21im2021-03-26 18:18:16,809 train.py[line:364] INFO: Checkpoint 37 saved !\n",
            "Epoch 37/50: 100%|█| 606/606 [02:03<00:00,  4.91im\n",
            "Epoch 38/50:   3%| | 16/606 [00:05<02:50,  3.47img2021-03-26 18:18:22,699 train.py[line:353] INFO: Train step_11220: loss : 18.940242767333984,loss xy : 8.958197593688965,loss wh : 0.9465923309326172,loss obj : 5.850164890289307，loss cls : 3.185288429260254,loss l2 : 4.621161460876465,lr : 0.001\n",
            "Epoch 38/50:   9%| | 56/606 [00:13<01:48,  5.06img2021-03-26 18:18:30,644 train.py[line:353] INFO: Train step_11240: loss : 23.978595733642578,loss xy : 12.612637519836426,loss wh : 0.863315999507904,loss obj : 9.772111892700195，loss cls : 0.7305298447608948,loss l2 : 4.695555210113525,lr : 0.001\n",
            "Epoch 38/50:  16%|▏| 96/606 [00:21<01:42,  4.98img2021-03-26 18:18:38,688 train.py[line:353] INFO: Train step_11260: loss : 25.874773025512695,loss xy : 10.90576171875,loss wh : 0.2599463164806366,loss obj : 12.36844253540039，loss cls : 2.340623617172241,loss l2 : 3.9016551971435547,lr : 0.001\n",
            "Epoch 38/50:  22%|▏| 136/606 [00:29<01:32,  5.06im2021-03-26 18:18:46,712 train.py[line:353] INFO: Train step_11280: loss : 6.290794849395752,loss xy : 4.190751075744629,loss wh : 0.1525348424911499,loss obj : 1.9432224035263062，loss cls : 0.004286210052669048,loss l2 : 0.827988862991333,lr : 0.001\n",
            "Epoch 38/50:  29%|▎| 176/606 [00:37<01:26,  4.97im2021-03-26 18:18:54,720 train.py[line:353] INFO: Train step_11300: loss : 21.173187255859375,loss xy : 14.456375122070312,loss wh : 0.5558662414550781,loss obj : 5.330498218536377，loss cls : 0.8304489850997925,loss l2 : 3.162550449371338,lr : 0.001\n",
            "Epoch 38/50:  36%|▎| 216/606 [00:45<01:16,  5.07im2021-03-26 18:19:02,688 train.py[line:353] INFO: Train step_11320: loss : 22.29035758972168,loss xy : 14.534086227416992,loss wh : 0.2205289900302887,loss obj : 6.267014503479004，loss cls : 1.268728256225586,loss l2 : 2.2923848628997803,lr : 0.001\n",
            "Epoch 38/50:  42%|▍| 256/606 [00:53<01:08,  5.07im2021-03-26 18:19:10,593 train.py[line:353] INFO: Train step_11340: loss : 9.576054573059082,loss xy : 5.895924091339111,loss wh : 0.08837499469518661,loss obj : 1.9387248754501343，loss cls : 1.6530301570892334,loss l2 : 1.2074702978134155,lr : 0.001\n",
            "Epoch 38/50:  49%|▍| 296/606 [01:01<01:01,  5.00im2021-03-26 18:19:18,471 train.py[line:353] INFO: Train step_11360: loss : 15.454854965209961,loss xy : 10.854331970214844,loss wh : 0.3999994397163391,loss obj : 4.186933517456055，loss cls : 0.013589462265372276,loss l2 : 2.181412696838379,lr : 0.001\n",
            "Epoch 38/50:  55%|▌| 336/606 [01:09<00:53,  5.04im2021-03-26 18:19:26,440 train.py[line:353] INFO: Train step_11380: loss : 21.14067268371582,loss xy : 11.670039176940918,loss wh : 0.5605437159538269,loss obj : 8.616476058959961，loss cls : 0.29361361265182495,loss l2 : 2.6521897315979004,lr : 0.001\n",
            "Epoch 38/50:  62%|▌| 376/606 [01:17<00:45,  5.09im2021-03-26 18:19:34,403 train.py[line:353] INFO: Train step_11400: loss : 12.928858757019043,loss xy : 7.442615509033203,loss wh : 0.03110191412270069,loss obj : 5.443751811981201，loss cls : 0.01138983853161335,loss l2 : 1.8544654846191406,lr : 0.001\n",
            "Epoch 38/50:  69%|▋| 416/606 [01:25<00:37,  5.00im2021-03-26 18:19:42,353 train.py[line:353] INFO: Train step_11420: loss : 43.506202697753906,loss xy : 24.057668685913086,loss wh : 0.7663651704788208,loss obj : 16.74166488647461，loss cls : 1.9405063390731812,loss l2 : 7.25485897064209,lr : 0.001\n",
            "Epoch 38/50:  75%|▊| 456/606 [01:33<00:30,  5.00im2021-03-26 18:19:50,260 train.py[line:353] INFO: Train step_11440: loss : 8.919677734375,loss xy : 7.620208740234375,loss wh : 0.019739093258976936,loss obj : 1.1555275917053223，loss cls : 0.12420283257961273,loss l2 : 0.20799176394939423,lr : 0.001\n",
            "Epoch 38/50:  82%|▊| 496/606 [01:40<00:21,  5.10im2021-03-26 18:19:58,174 train.py[line:353] INFO: Train step_11460: loss : 12.515837669372559,loss xy : 8.156725883483887,loss wh : 0.0920020043849945,loss obj : 4.222472667694092，loss cls : 0.04463681951165199,loss l2 : 1.6212631464004517,lr : 0.001\n",
            "Epoch 38/50:  88%|▉| 536/606 [01:49<00:13,  5.02im2021-03-26 18:20:06,243 train.py[line:353] INFO: Train step_11480: loss : 24.88067626953125,loss xy : 7.154716491699219,loss wh : 0.20840376615524292,loss obj : 17.191085815429688，loss cls : 0.32646965980529785,loss l2 : 4.791543483734131,lr : 0.001\n",
            "Epoch 38/50:  95%|▉| 576/606 [01:56<00:05,  5.11im2021-03-26 18:20:14,101 train.py[line:353] INFO: Train step_11500: loss : 29.089736938476562,loss xy : 9.615640640258789,loss wh : 0.3413516879081726,loss obj : 16.726633071899414，loss cls : 2.406111717224121,loss l2 : 6.555410385131836,lr : 0.001\n",
            "Epoch 38/50: 100%|█| 606/606 [02:02<00:00,  5.18im2021-03-26 18:20:20,018 train.py[line:364] INFO: Checkpoint 38 saved !\n",
            "Epoch 38/50: 100%|█| 606/606 [02:03<00:00,  4.92im\n",
            "Epoch 39/50:   2%| | 10/606 [00:04<04:33,  2.18img2021-03-26 18:20:24,562 train.py[line:353] INFO: Train step_11520: loss : 22.084501266479492,loss xy : 8.564679145812988,loss wh : 1.2413570880889893,loss obj : 8.765819549560547，loss cls : 3.5126450061798096,loss l2 : 5.2803120613098145,lr : 0.001\n",
            "Epoch 39/50:   8%| | 50/606 [00:12<01:49,  5.06img2021-03-26 18:20:32,479 train.py[line:353] INFO: Train step_11540: loss : 39.17071533203125,loss xy : 25.299144744873047,loss wh : 0.6818361878395081,loss obj : 13.047074317932129，loss cls : 0.14265654981136322,loss l2 : 4.902590274810791,lr : 0.001\n",
            "Epoch 39/50:  15%|▏| 90/606 [00:19<01:43,  4.97img2021-03-26 18:20:40,434 train.py[line:353] INFO: Train step_11560: loss : 20.723543167114258,loss xy : 12.079483032226562,loss wh : 0.4396727681159973,loss obj : 7.831075191497803，loss cls : 0.3733116686344147,loss l2 : 3.3843986988067627,lr : 0.001\n",
            "Epoch 39/50:  21%|▏| 130/606 [00:28<01:36,  4.95im2021-03-26 18:20:48,447 train.py[line:353] INFO: Train step_11580: loss : 12.805047035217285,loss xy : 9.205547332763672,loss wh : 0.23012246191501617,loss obj : 3.2976763248443604，loss cls : 0.07170137763023376,loss l2 : 1.5509922504425049,lr : 0.001\n",
            "Epoch 39/50:  28%|▎| 170/606 [00:35<01:27,  5.00im2021-03-26 18:20:56,415 train.py[line:353] INFO: Train step_11600: loss : 22.770654678344727,loss xy : 13.923515319824219,loss wh : 0.5077933073043823,loss obj : 8.218295097351074，loss cls : 0.12104938924312592,loss l2 : 3.2958855628967285,lr : 0.001\n",
            "Epoch 39/50:  35%|▎| 210/606 [00:43<01:18,  5.01im2021-03-26 18:21:04,359 train.py[line:353] INFO: Train step_11620: loss : 19.411235809326172,loss xy : 11.038796424865723,loss wh : 0.5644052624702454,loss obj : 6.5277862548828125，loss cls : 1.280246376991272,loss l2 : 3.1032183170318604,lr : 0.001\n",
            "Epoch 39/50:  41%|▍| 250/606 [00:51<01:11,  4.99im2021-03-26 18:21:12,313 train.py[line:353] INFO: Train step_11640: loss : 22.351661682128906,loss xy : 15.820100784301758,loss wh : 0.4018988311290741,loss obj : 6.06214714050293，loss cls : 0.06751500815153122,loss l2 : 2.579815626144409,lr : 0.001\n",
            "Epoch 39/50:  48%|▍| 290/606 [00:59<01:03,  4.97im2021-03-26 18:21:20,294 train.py[line:353] INFO: Train step_11660: loss : 7.731363773345947,loss xy : 5.368720054626465,loss wh : 0.17517492175102234,loss obj : 2.181528091430664，loss cls : 0.005940915551036596,loss l2 : 1.1189912557601929,lr : 0.001\n",
            "Epoch 39/50:  54%|▌| 330/606 [01:07<00:54,  5.04im2021-03-26 18:21:28,261 train.py[line:353] INFO: Train step_11680: loss : 11.379594802856445,loss xy : 6.985136032104492,loss wh : 0.11484705656766891,loss obj : 4.147889137268066，loss cls : 0.13172228634357452,loss l2 : 1.5696437358856201,lr : 0.001\n",
            "Epoch 39/50:  61%|▌| 370/606 [01:15<00:45,  5.14im2021-03-26 18:21:36,158 train.py[line:353] INFO: Train step_11700: loss : 27.06956672668457,loss xy : 20.009761810302734,loss wh : 0.3307444453239441,loss obj : 6.300826072692871，loss cls : 0.4282366931438446,loss l2 : 3.1067910194396973,lr : 0.001\n",
            "Epoch 39/50:  68%|▋| 410/606 [01:23<00:39,  5.00im2021-03-26 18:21:44,113 train.py[line:353] INFO: Train step_11720: loss : 9.381261825561523,loss xy : 5.988801956176758,loss wh : 0.13403643667697906,loss obj : 2.6540143489837646，loss cls : 0.6044088006019592,loss l2 : 1.3489785194396973,lr : 0.001\n",
            "Epoch 39/50:  74%|▋| 450/606 [01:31<00:30,  5.11im2021-03-26 18:21:51,996 train.py[line:353] INFO: Train step_11740: loss : 23.370990753173828,loss xy : 10.682968139648438,loss wh : 0.4131042957305908,loss obj : 12.172576904296875，loss cls : 0.10234110802412033,loss l2 : 2.6070144176483154,lr : 0.001\n",
            "Epoch 39/50:  81%|▊| 490/606 [01:39<00:22,  5.09im2021-03-26 18:21:59,945 train.py[line:353] INFO: Train step_11760: loss : 32.047637939453125,loss xy : 13.691658020019531,loss wh : 0.8037766218185425,loss obj : 15.32196044921875，loss cls : 2.230242967605591,loss l2 : 5.985826015472412,lr : 0.001\n",
            "Epoch 39/50:  87%|▊| 530/606 [01:47<00:15,  4.91im2021-03-26 18:22:07,966 train.py[line:353] INFO: Train step_11780: loss : 9.595845222473145,loss xy : 6.035015106201172,loss wh : 0.08643943071365356,loss obj : 3.4240047931671143，loss cls : 0.05038540065288544,loss l2 : 1.238515853881836,lr : 0.001\n",
            "Epoch 39/50:  94%|▉| 570/606 [01:55<00:07,  5.09im2021-03-26 18:22:15,879 train.py[line:353] INFO: Train step_11800: loss : 14.640871047973633,loss xy : 11.040130615234375,loss wh : 0.37427186965942383,loss obj : 3.0996789932250977，loss cls : 0.12678983807563782,loss l2 : 1.655051827430725,lr : 0.001\n",
            "Epoch 39/50: 100%|█| 606/606 [02:02<00:00,  5.24im2021-03-26 18:22:22,949 train.py[line:364] INFO: Checkpoint 39 saved !\n",
            "Epoch 39/50: 100%|█| 606/606 [02:02<00:00,  4.93im\n",
            "Epoch 40/50:   1%| | 4/606 [00:03<10:00,  1.00img/2021-03-26 18:22:26,499 train.py[line:353] INFO: Train step_11820: loss : 22.707883834838867,loss xy : 12.79374885559082,loss wh : 0.49778884649276733,loss obj : 9.334378242492676，loss cls : 0.08196740597486496,loss l2 : 4.517026901245117,lr : 0.001\n",
            "Epoch 40/50:   7%| | 44/606 [00:11<01:51,  5.03img2021-03-26 18:22:34,421 train.py[line:353] INFO: Train step_11840: loss : 27.749439239501953,loss xy : 14.48204231262207,loss wh : 0.9162338972091675,loss obj : 11.182350158691406，loss cls : 1.1688114404678345,loss l2 : 4.7801666259765625,lr : 0.001\n",
            "Epoch 40/50:  14%|▏| 84/606 [00:18<01:41,  5.14img2021-03-26 18:22:42,285 train.py[line:353] INFO: Train step_11860: loss : 26.5228214263916,loss xy : 13.948229789733887,loss wh : 0.8644605875015259,loss obj : 11.19526195526123，loss cls : 0.5148677229881287,loss l2 : 4.535656452178955,lr : 0.001\n",
            "Epoch 40/50:  20%|▏| 124/606 [00:26<01:35,  5.07im2021-03-26 18:22:50,171 train.py[line:353] INFO: Train step_11880: loss : 52.494754791259766,loss xy : 27.690628051757812,loss wh : 1.386932134628296,loss obj : 17.362470626831055，loss cls : 6.054721355438232,loss l2 : 8.761250495910645,lr : 0.001\n",
            "Epoch 40/50:  27%|▎| 164/606 [00:34<01:27,  5.03im2021-03-26 18:22:58,121 train.py[line:353] INFO: Train step_11900: loss : 9.807900428771973,loss xy : 7.194130897521973,loss wh : 0.4655497670173645,loss obj : 2.1211345195770264，loss cls : 0.02708493545651436,loss l2 : 1.387108325958252,lr : 0.001\n",
            "Epoch 40/50:  34%|▎| 204/606 [00:42<01:18,  5.12im2021-03-26 18:23:06,000 train.py[line:353] INFO: Train step_11920: loss : 28.043354034423828,loss xy : 11.562651634216309,loss wh : 0.7582470178604126,loss obj : 14.797380447387695，loss cls : 0.9250759482383728,loss l2 : 5.853285312652588,lr : 0.001\n",
            "Epoch 40/50:  40%|▍| 244/606 [00:50<01:11,  5.03im2021-03-26 18:23:14,005 train.py[line:353] INFO: Train step_11940: loss : 15.694534301757812,loss xy : 6.964214324951172,loss wh : 0.3861171007156372,loss obj : 7.849966526031494，loss cls : 0.494235634803772,loss l2 : 2.1386051177978516,lr : 0.001\n",
            "Epoch 40/50:  47%|▍| 284/606 [00:58<01:04,  5.02im2021-03-26 18:23:22,003 train.py[line:353] INFO: Train step_11960: loss : 23.515357971191406,loss xy : 15.922812461853027,loss wh : 0.8306881189346313,loss obj : 6.405447006225586，loss cls : 0.3564108908176422,loss l2 : 3.446011543273926,lr : 0.001\n",
            "Epoch 40/50:  53%|▌| 324/606 [01:06<00:55,  5.06im2021-03-26 18:23:29,870 train.py[line:353] INFO: Train step_11980: loss : 12.618023872375488,loss xy : 9.392158508300781,loss wh : 0.15092431008815765,loss obj : 3.0471692085266113，loss cls : 0.027771756052970886,loss l2 : 1.1024837493896484,lr : 0.001\n",
            "Epoch 40/50:  60%|▌| 364/606 [01:14<00:47,  5.09im2021-03-26 18:23:37,757 train.py[line:353] INFO: Train step_12000: loss : 14.002694129943848,loss xy : 11.773134231567383,loss wh : 0.2980143427848816,loss obj : 1.9060518741607666，loss cls : 0.025493372231721878,loss l2 : 1.2477279901504517,lr : 0.001\n",
            "Epoch 40/50:  67%|▋| 404/606 [01:22<00:39,  5.08im2021-03-26 18:23:45,640 train.py[line:353] INFO: Train step_12020: loss : 41.09373474121094,loss xy : 17.478702545166016,loss wh : 1.4758485555648804,loss obj : 14.204486846923828，loss cls : 7.934694290161133,loss l2 : 9.214738845825195,lr : 0.001\n",
            "Epoch 40/50:  73%|▋| 444/606 [01:30<00:32,  4.97im2021-03-26 18:23:53,561 train.py[line:353] INFO: Train step_12040: loss : 22.242631912231445,loss xy : 6.229223251342773,loss wh : 0.22621193528175354,loss obj : 14.688637733459473，loss cls : 1.0985583066940308,loss l2 : 3.1773781776428223,lr : 0.001\n",
            "Epoch 40/50:  80%|▊| 484/606 [01:38<00:23,  5.12im2021-03-26 18:24:01,429 train.py[line:353] INFO: Train step_12060: loss : 8.654047966003418,loss xy : 5.746356964111328,loss wh : 0.2567952275276184,loss obj : 2.637439727783203，loss cls : 0.013455163687467575,loss l2 : 1.470472812652588,lr : 0.001\n",
            "Epoch 40/50:  86%|▊| 524/606 [01:45<00:16,  5.09im2021-03-26 18:24:09,337 train.py[line:353] INFO: Train step_12080: loss : 6.931726932525635,loss xy : 5.547067642211914,loss wh : 0.15625880658626556,loss obj : 1.2274748086929321，loss cls : 0.0009259439539164305,loss l2 : 0.3992796838283539,lr : 0.001\n",
            "Epoch 40/50:  93%|▉| 564/606 [01:53<00:08,  5.03im2021-03-26 18:24:17,280 train.py[line:353] INFO: Train step_12100: loss : 10.722230911254883,loss xy : 7.406883716583252,loss wh : 0.15434718132019043,loss obj : 2.0285422801971436，loss cls : 1.1324573755264282,loss l2 : 1.241350769996643,lr : 0.001\n",
            "Epoch 40/50: 100%|▉| 604/606 [02:01<00:00,  5.21im2021-03-26 18:24:25,033 train.py[line:353] INFO: Train step_12120: loss : 9.20090389251709,loss xy : 7.531431674957275,loss wh : 0.05191699042916298,loss obj : 1.307464599609375，loss cls : 0.31008991599082947,loss l2 : 0.357230007648468,lr : 0.001\n",
            "Epoch 40/50: 100%|█| 606/606 [02:02<00:00,  5.19im2021-03-26 18:24:25,569 train.py[line:364] INFO: Checkpoint 40 saved !\n",
            "Epoch 40/50: 100%|█| 606/606 [02:02<00:00,  4.94im\n",
            "Epoch 41/50:   6%| | 38/606 [00:10<01:53,  5.02img2021-03-26 18:24:35,976 train.py[line:353] INFO: Train step_12140: loss : 16.074565887451172,loss xy : 11.120096206665039,loss wh : 0.10338985919952393,loss obj : 4.531778812408447，loss cls : 0.31930071115493774,loss l2 : 1.3785127401351929,lr : 0.001\n",
            "Epoch 41/50:  13%|▏| 78/606 [00:17<01:44,  5.04img2021-03-26 18:24:43,888 train.py[line:353] INFO: Train step_12160: loss : 19.163721084594727,loss xy : 12.080856323242188,loss wh : 0.3183967173099518,loss obj : 6.6479997634887695，loss cls : 0.11646872758865356,loss l2 : 2.813462734222412,lr : 0.001\n",
            "Epoch 41/50:  19%|▏| 118/606 [00:25<01:36,  5.08im2021-03-26 18:24:51,771 train.py[line:353] INFO: Train step_12180: loss : 9.228472709655762,loss xy : 6.227753162384033,loss wh : 0.473297655582428,loss obj : 2.522926092147827，loss cls : 0.004495364613831043,loss l2 : 1.541934847831726,lr : 0.001\n",
            "Epoch 41/50:  26%|▎| 158/606 [00:33<01:29,  5.03im2021-03-26 18:24:59,737 train.py[line:353] INFO: Train step_12200: loss : 30.254131317138672,loss xy : 19.263830184936523,loss wh : 1.1482610702514648,loss obj : 8.393362998962402，loss cls : 1.4486773014068604,loss l2 : 5.174732685089111,lr : 0.001\n",
            "Epoch 41/50:  33%|▎| 198/606 [00:41<01:21,  5.02im2021-03-26 18:25:07,680 train.py[line:353] INFO: Train step_12220: loss : 29.104185104370117,loss xy : 20.75010871887207,loss wh : 0.8156419992446899,loss obj : 6.437592506408691，loss cls : 1.1008436679840088,loss l2 : 4.098659515380859,lr : 0.001\n",
            "Epoch 41/50:  39%|▍| 238/606 [00:49<01:12,  5.05im2021-03-26 18:25:15,613 train.py[line:353] INFO: Train step_12240: loss : 9.525659561157227,loss xy : 7.178813934326172,loss wh : 0.20678593218326569,loss obj : 1.3785251379013062，loss cls : 0.7615346312522888,loss l2 : 1.0424546003341675,lr : 0.001\n",
            "Epoch 41/50:  46%|▍| 278/606 [00:57<01:05,  4.98im2021-03-26 18:25:23,513 train.py[line:353] INFO: Train step_12260: loss : 16.358154296875,loss xy : 5.336413383483887,loss wh : 0.5878438949584961,loss obj : 9.874435424804688，loss cls : 0.5594612956047058,loss l2 : 2.4885478019714355,lr : 0.001\n",
            "Epoch 41/50:  52%|▌| 318/606 [01:05<00:57,  5.02im2021-03-26 18:25:31,484 train.py[line:353] INFO: Train step_12280: loss : 45.070499420166016,loss xy : 15.391406059265137,loss wh : 3.4087154865264893,loss obj : 20.598886489868164，loss cls : 5.671490669250488,loss l2 : 12.486096382141113,lr : 0.001\n",
            "Epoch 41/50:  59%|▌| 358/606 [01:13<00:48,  5.08im2021-03-26 18:25:39,468 train.py[line:353] INFO: Train step_12300: loss : 32.30910873413086,loss xy : 18.93760871887207,loss wh : 0.24438810348510742,loss obj : 9.6066312789917，loss cls : 3.520482063293457,loss l2 : 5.178754806518555,lr : 0.001\n",
            "Epoch 41/50:  66%|▋| 398/606 [01:21<00:40,  5.10im2021-03-26 18:25:47,352 train.py[line:353] INFO: Train step_12320: loss : 21.277671813964844,loss xy : 17.014312744140625,loss wh : 0.5191333889961243,loss obj : 3.729858875274658，loss cls : 0.014368518255650997,loss l2 : 2.183384418487549,lr : 0.001\n",
            "Epoch 41/50:  72%|▋| 438/606 [01:29<00:33,  5.07im2021-03-26 18:25:55,293 train.py[line:353] INFO: Train step_12340: loss : 4.888504981994629,loss xy : 3.5670840740203857,loss wh : 0.023481208831071854,loss obj : 1.258724570274353，loss cls : 0.03921513259410858,loss l2 : 0.16924656927585602,lr : 0.001\n",
            "Epoch 41/50:  79%|▊| 478/606 [01:37<00:25,  4.98im2021-03-26 18:26:03,255 train.py[line:353] INFO: Train step_12360: loss : 29.986061096191406,loss xy : 9.606024742126465,loss wh : 2.5081794261932373,loss obj : 10.076024055480957，loss cls : 7.795833587646484,loss l2 : 10.179677963256836,lr : 0.001\n",
            "Epoch 41/50:  85%|▊| 518/606 [01:45<00:17,  5.02im2021-03-26 18:26:11,242 train.py[line:353] INFO: Train step_12380: loss : 9.474860191345215,loss xy : 7.389777183532715,loss wh : 0.4482495188713074,loss obj : 1.6171348094940186，loss cls : 0.01969931647181511,loss l2 : 1.1245636940002441,lr : 0.001\n",
            "Epoch 41/50:  92%|▉| 558/606 [01:53<00:09,  5.04im2021-03-26 18:26:19,184 train.py[line:353] INFO: Train step_12400: loss : 28.951963424682617,loss xy : 22.496477127075195,loss wh : 0.5380611419677734,loss obj : 5.614501953125，loss cls : 0.30292296409606934,loss l2 : 3.1109511852264404,lr : 0.001\n",
            "Epoch 41/50:  99%|▉| 598/606 [02:01<00:01,  5.19im2021-03-26 18:26:26,966 train.py[line:353] INFO: Train step_12420: loss : 37.13186264038086,loss xy : 25.079936981201172,loss wh : 0.7571004629135132,loss obj : 10.311635971069336，loss cls : 0.9831876158714294,loss l2 : 5.370151042938232,lr : 0.001\n",
            "Epoch 41/50: 100%|█| 606/606 [02:02<00:00,  5.20im2021-03-26 18:26:28,718 train.py[line:364] INFO: Checkpoint 41 saved !\n",
            "Epoch 41/50: 100%|█| 606/606 [02:03<00:00,  4.92im\n",
            "Epoch 42/50:   5%| | 32/606 [00:08<01:56,  4.93img2021-03-26 18:26:37,814 train.py[line:353] INFO: Train step_12440: loss : 14.38116455078125,loss xy : 12.44387435913086,loss wh : 0.1506529301404953,loss obj : 1.7629896402359009，loss cls : 0.023647157475352287,loss l2 : 1.0612640380859375,lr : 0.001\n",
            "Epoch 42/50:  12%| | 72/606 [00:16<01:45,  5.06img2021-03-26 18:26:45,739 train.py[line:353] INFO: Train step_12460: loss : 26.681684494018555,loss xy : 19.985939025878906,loss wh : 0.6564608812332153,loss obj : 5.917355537414551，loss cls : 0.12192820012569427,loss l2 : 3.4384098052978516,lr : 0.001\n",
            "Epoch 42/50:  18%|▏| 112/606 [00:24<01:38,  5.02im2021-03-26 18:26:53,732 train.py[line:353] INFO: Train step_12480: loss : 21.59360122680664,loss xy : 9.815311431884766,loss wh : 0.45113232731819153,loss obj : 10.734186172485352，loss cls : 0.5929720997810364,loss l2 : 3.891631841659546,lr : 0.001\n",
            "Epoch 42/50:  25%|▎| 152/606 [00:32<01:30,  4.99im2021-03-26 18:27:01,705 train.py[line:353] INFO: Train step_12500: loss : 22.965654373168945,loss xy : 16.00082778930664,loss wh : 0.3505011796951294,loss obj : 4.254020690917969，loss cls : 2.3603038787841797,loss l2 : 3.0555200576782227,lr : 0.001\n",
            "Epoch 42/50:  32%|▎| 192/606 [00:40<01:23,  4.95im2021-03-26 18:27:09,707 train.py[line:353] INFO: Train step_12520: loss : 8.198680877685547,loss xy : 6.424151420593262,loss wh : 0.03458547219634056,loss obj : 1.7395718097686768，loss cls : 0.0003717120853252709,loss l2 : 0.5638313889503479,lr : 0.001\n",
            "Epoch 42/50:  38%|▍| 232/606 [00:48<01:15,  4.99im2021-03-26 18:27:17,702 train.py[line:353] INFO: Train step_12540: loss : 29.863353729248047,loss xy : 24.868444442749023,loss wh : 0.5410422682762146,loss obj : 4.0528740882873535，loss cls : 0.40099382400512695,loss l2 : 2.5176315307617188,lr : 0.001\n",
            "Epoch 42/50:  45%|▍| 272/606 [00:56<01:05,  5.09im2021-03-26 18:27:25,598 train.py[line:353] INFO: Train step_12560: loss : 14.528465270996094,loss xy : 12.66184139251709,loss wh : 0.23093856871128082,loss obj : 1.6180528402328491，loss cls : 0.017632529139518738,loss l2 : 1.0013985633850098,lr : 0.001\n",
            "Epoch 42/50:  51%|▌| 312/606 [01:04<00:58,  5.00im2021-03-26 18:27:33,623 train.py[line:353] INFO: Train step_12580: loss : 15.5512056350708,loss xy : 10.01365852355957,loss wh : 0.48113468289375305,loss obj : 5.014699935913086，loss cls : 0.04171264171600342,loss l2 : 2.519517421722412,lr : 0.001\n",
            "Epoch 42/50:  58%|▌| 352/606 [01:12<00:49,  5.09im2021-03-26 18:27:41,464 train.py[line:353] INFO: Train step_12600: loss : 16.224390029907227,loss xy : 6.037132263183594,loss wh : 0.3181423842906952,loss obj : 9.739480018615723，loss cls : 0.1296345740556717,loss l2 : 3.319272518157959,lr : 0.001\n",
            "Epoch 42/50:  65%|▋| 392/606 [01:20<00:42,  5.01im2021-03-26 18:27:49,394 train.py[line:353] INFO: Train step_12620: loss : 34.989418029785156,loss xy : 16.68436050415039,loss wh : 0.712551474571228,loss obj : 15.275235176086426，loss cls : 2.317272901535034,loss l2 : 5.712607383728027,lr : 0.001\n",
            "Epoch 42/50:  71%|▋| 432/606 [01:28<00:34,  5.08im2021-03-26 18:27:57,260 train.py[line:353] INFO: Train step_12640: loss : 7.626895427703857,loss xy : 5.817188262939453,loss wh : 0.09945900738239288,loss obj : 1.648323655128479，loss cls : 0.061924271285533905,loss l2 : 0.8161504864692688,lr : 0.001\n",
            "Epoch 42/50:  78%|▊| 472/606 [01:36<00:26,  4.99im2021-03-26 18:28:05,212 train.py[line:353] INFO: Train step_12660: loss : 8.040847778320312,loss xy : 6.995529651641846,loss wh : 0.13558310270309448,loss obj : 0.8511742353439331，loss cls : 0.058560438454151154,loss l2 : 0.4843918979167938,lr : 0.001\n",
            "Epoch 42/50:  84%|▊| 512/606 [01:44<00:18,  5.01im2021-03-26 18:28:13,141 train.py[line:353] INFO: Train step_12680: loss : 9.960905075073242,loss xy : 2.6218996047973633,loss wh : 0.020208794623613358,loss obj : 7.316599369049072，loss cls : 0.0021974726114422083,loss l2 : 2.3374462127685547,lr : 0.001\n",
            "Epoch 42/50:  91%|▉| 552/606 [01:51<00:10,  4.99im2021-03-26 18:28:21,097 train.py[line:353] INFO: Train step_12700: loss : 9.539875984191895,loss xy : 7.286259651184082,loss wh : 0.5011484622955322,loss obj : 1.7190356254577637，loss cls : 0.03343316912651062,loss l2 : 1.4447966814041138,lr : 0.001\n",
            "Epoch 42/50:  98%|▉| 592/606 [01:59<00:02,  5.21im2021-03-26 18:28:28,895 train.py[line:353] INFO: Train step_12720: loss : 21.326505661010742,loss xy : 15.659149169921875,loss wh : 0.2783418297767639,loss obj : 5.355965614318848，loss cls : 0.033049117773771286,loss l2 : 2.5031139850616455,lr : 0.001\n",
            "Epoch 42/50: 100%|█| 606/606 [02:02<00:00,  5.20im2021-03-26 18:28:31,798 train.py[line:364] INFO: Checkpoint 42 saved !\n",
            "Epoch 42/50: 100%|█| 606/606 [02:03<00:00,  4.92im\n",
            "Epoch 43/50:   4%| | 26/606 [00:07<02:04,  4.66img2021-03-26 18:28:40,039 train.py[line:353] INFO: Train step_12740: loss : 37.295230865478516,loss xy : 18.786907196044922,loss wh : 0.9917988777160645,loss obj : 10.507694244384766，loss cls : 7.0088300704956055,loss l2 : 7.300952434539795,lr : 0.001\n",
            "Epoch 43/50:  11%| | 66/606 [00:15<01:48,  4.97img2021-03-26 18:28:47,986 train.py[line:353] INFO: Train step_12760: loss : 33.173858642578125,loss xy : 17.741294860839844,loss wh : 0.9709110260009766,loss obj : 12.461986541748047，loss cls : 1.999668002128601,loss l2 : 6.095129013061523,lr : 0.001\n",
            "Epoch 43/50:  17%|▏| 106/606 [00:23<01:38,  5.07im2021-03-26 18:28:55,929 train.py[line:353] INFO: Train step_12780: loss : 20.39159393310547,loss xy : 12.996140480041504,loss wh : 0.19409175217151642,loss obj : 4.453368663787842，loss cls : 2.747992515563965,loss l2 : 3.0188422203063965,lr : 0.001\n",
            "Epoch 43/50:  24%|▏| 146/606 [00:31<01:30,  5.08im2021-03-26 18:29:03,873 train.py[line:353] INFO: Train step_12800: loss : 20.862300872802734,loss xy : 15.216558456420898,loss wh : 0.337588906288147,loss obj : 4.9837846755981445，loss cls : 0.324368417263031,loss l2 : 2.374871015548706,lr : 0.001\n",
            "Epoch 43/50:  31%|▎| 186/606 [00:39<01:23,  5.01im2021-03-26 18:29:11,859 train.py[line:353] INFO: Train step_12820: loss : 19.734766006469727,loss xy : 6.086661338806152,loss wh : 0.40572455525398254,loss obj : 10.874263763427734，loss cls : 2.368117094039917,loss l2 : 4.095830917358398,lr : 0.001\n",
            "Epoch 43/50:  37%|▎| 226/606 [00:47<01:15,  5.03im2021-03-26 18:29:19,789 train.py[line:353] INFO: Train step_12840: loss : 62.901580810546875,loss xy : 33.618980407714844,loss wh : 2.662513017654419,loss obj : 24.626766204833984，loss cls : 1.9933223724365234,loss l2 : 11.593317031860352,lr : 0.001\n",
            "Epoch 43/50:  44%|▍| 266/606 [00:55<01:07,  5.03im2021-03-26 18:29:27,773 train.py[line:353] INFO: Train step_12860: loss : 12.095782279968262,loss xy : 7.623953819274902,loss wh : 0.21012923121452332,loss obj : 4.053049087524414，loss cls : 0.2086499035358429,loss l2 : 1.4551717042922974,lr : 0.001\n",
            "Epoch 43/50:  50%|▌| 306/606 [01:03<01:00,  5.00im2021-03-26 18:29:35,741 train.py[line:353] INFO: Train step_12880: loss : 13.861593246459961,loss xy : 7.880732536315918,loss wh : 0.4121885299682617,loss obj : 5.532347202301025，loss cls : 0.03632500767707825,loss l2 : 2.38606858253479,lr : 0.001\n",
            "Epoch 43/50:  57%|▌| 346/606 [01:11<00:51,  5.04im2021-03-26 18:29:43,715 train.py[line:353] INFO: Train step_12900: loss : 13.933420181274414,loss xy : 10.401634216308594,loss wh : 0.1444292515516281,loss obj : 3.21574330329895，loss cls : 0.17161357402801514,loss l2 : 1.4033114910125732,lr : 0.001\n",
            "Epoch 43/50:  64%|▋| 386/606 [01:19<00:43,  5.01im2021-03-26 18:29:51,684 train.py[line:353] INFO: Train step_12920: loss : 17.147579193115234,loss xy : 9.61848258972168,loss wh : 0.29304707050323486,loss obj : 7.228816986083984，loss cls : 0.007231954950839281,loss l2 : 1.7523057460784912,lr : 0.001\n",
            "Epoch 43/50:  70%|▋| 426/606 [01:27<00:35,  5.05im2021-03-26 18:29:59,650 train.py[line:353] INFO: Train step_12940: loss : 15.437790870666504,loss xy : 8.13808536529541,loss wh : 0.08630462735891342,loss obj : 6.255756378173828，loss cls : 0.9576444625854492,loss l2 : 2.583348512649536,lr : 0.001\n",
            "Epoch 43/50:  77%|▊| 466/606 [01:35<00:27,  5.10im2021-03-26 18:30:07,650 train.py[line:353] INFO: Train step_12960: loss : 12.924939155578613,loss xy : 9.600264549255371,loss wh : 0.15723730623722076,loss obj : 3.1365954875946045，loss cls : 0.03084203228354454,loss l2 : 0.9768753051757812,lr : 0.001\n",
            "Epoch 43/50:  83%|▊| 506/606 [01:43<00:20,  4.97im2021-03-26 18:30:15,648 train.py[line:353] INFO: Train step_12980: loss : 26.940448760986328,loss xy : 19.87297821044922,loss wh : 0.5104100108146667,loss obj : 6.4804792404174805，loss cls : 0.07657936215400696,loss l2 : 3.179762125015259,lr : 0.001\n",
            "Epoch 43/50:  90%|▉| 546/606 [01:51<00:11,  5.03im2021-03-26 18:30:23,581 train.py[line:353] INFO: Train step_13000: loss : 6.541844844818115,loss xy : 5.022910118103027,loss wh : 0.05636356398463249,loss obj : 1.460277795791626，loss cls : 0.00229303864762187,loss l2 : 0.4444476068019867,lr : 0.001\n",
            "Epoch 43/50:  97%|▉| 586/606 [01:59<00:03,  5.16im2021-03-26 18:30:31,571 train.py[line:353] INFO: Train step_13020: loss : 20.94667625427246,loss xy : 12.114360809326172,loss wh : 0.2517092823982239,loss obj : 8.574810028076172，loss cls : 0.0057961600832641125,loss l2 : 2.485865831375122,lr : 0.001\n",
            "Epoch 43/50: 100%|█| 606/606 [02:03<00:00,  5.11im2021-03-26 18:30:35,604 train.py[line:364] INFO: Checkpoint 43 saved !\n",
            "Epoch 43/50: 100%|█| 606/606 [02:03<00:00,  4.89im\n",
            "Epoch 44/50:   3%| | 20/606 [00:06<02:28,  3.94img2021-03-26 18:30:42,793 train.py[line:353] INFO: Train step_13040: loss : 17.94139289855957,loss xy : 15.820537567138672,loss wh : 0.32293039560317993,loss obj : 1.7875622510910034，loss cls : 0.010363111272454262,loss l2 : 1.1976820230484009,lr : 0.001\n",
            "Epoch 44/50:  10%| | 60/606 [00:14<01:47,  5.09img2021-03-26 18:30:50,775 train.py[line:353] INFO: Train step_13060: loss : 42.673545837402344,loss xy : 21.79182243347168,loss wh : 1.7194054126739502,loss obj : 14.434728622436523，loss cls : 4.7275896072387695,loss l2 : 9.414894104003906,lr : 0.001\n",
            "Epoch 44/50:  17%|▏| 100/606 [00:22<01:40,  5.04im2021-03-26 18:30:58,775 train.py[line:353] INFO: Train step_13080: loss : 20.18058204650879,loss xy : 11.295044898986816,loss wh : 0.3762846887111664,loss obj : 8.271736145019531，loss cls : 0.2375161051750183,loss l2 : 2.887230634689331,lr : 0.001\n",
            "Epoch 44/50:  23%|▏| 140/606 [00:30<01:34,  4.93im2021-03-26 18:31:06,791 train.py[line:353] INFO: Train step_13100: loss : 36.66131591796875,loss xy : 22.49726104736328,loss wh : 1.433493971824646,loss obj : 11.428462982177734，loss cls : 1.3020987510681152,loss l2 : 7.133477210998535,lr : 0.001\n",
            "Epoch 44/50:  30%|▎| 180/606 [00:38<01:23,  5.10im2021-03-26 18:31:14,744 train.py[line:353] INFO: Train step_13120: loss : 26.113832473754883,loss xy : 18.94431495666504,loss wh : 0.7708869576454163,loss obj : 4.578747749328613，loss cls : 1.819884181022644,loss l2 : 4.064446449279785,lr : 0.001\n",
            "Epoch 44/50:  36%|▎| 220/606 [00:46<01:16,  5.03im2021-03-26 18:31:22,731 train.py[line:353] INFO: Train step_13140: loss : 12.660276412963867,loss xy : 8.14079761505127,loss wh : 0.07432333379983902,loss obj : 4.380234718322754，loss cls : 0.06492040306329727,loss l2 : 1.3575186729431152,lr : 0.001\n",
            "Epoch 44/50:  43%|▍| 260/606 [00:54<01:10,  4.89im2021-03-26 18:31:30,741 train.py[line:353] INFO: Train step_13160: loss : 8.654658317565918,loss xy : 5.546342372894287,loss wh : 0.09340585768222809,loss obj : 3.0097787380218506，loss cls : 0.00513178575783968,loss l2 : 1.4833407402038574,lr : 0.001\n",
            "Epoch 44/50:  50%|▍| 300/606 [01:02<01:01,  5.01im2021-03-26 18:31:38,698 train.py[line:353] INFO: Train step_13180: loss : 15.218955039978027,loss xy : 13.048635482788086,loss wh : 0.24845944344997406,loss obj : 1.9168927669525146，loss cls : 0.004966307897120714,loss l2 : 1.3095837831497192,lr : 0.001\n",
            "Epoch 44/50:  56%|▌| 340/606 [01:10<00:51,  5.16im2021-03-26 18:31:46,564 train.py[line:353] INFO: Train step_13200: loss : 23.404733657836914,loss xy : 12.769805908203125,loss wh : 0.8360195159912109,loss obj : 8.321931838989258，loss cls : 1.4769772291183472,loss l2 : 4.744220733642578,lr : 0.001\n",
            "Epoch 44/50:  63%|▋| 380/606 [01:18<00:44,  5.02im2021-03-26 18:31:54,498 train.py[line:353] INFO: Train step_13220: loss : 25.327207565307617,loss xy : 13.766641616821289,loss wh : 0.8480835556983948,loss obj : 10.036388397216797，loss cls : 0.676093578338623,loss l2 : 3.590003490447998,lr : 0.001\n",
            "Epoch 44/50:  69%|▋| 420/606 [01:26<00:36,  5.08im2021-03-26 18:32:02,414 train.py[line:353] INFO: Train step_13240: loss : 32.23699951171875,loss xy : 17.390663146972656,loss wh : 0.4161945581436157,loss obj : 12.313861846923828，loss cls : 2.116281032562256,loss l2 : 4.6564226150512695,lr : 0.001\n",
            "Epoch 44/50:  76%|▊| 460/606 [01:34<00:29,  4.91im2021-03-26 18:32:10,485 train.py[line:353] INFO: Train step_13260: loss : 15.256928443908691,loss xy : 8.6189603805542,loss wh : 0.06651435792446136,loss obj : 3.5945353507995605，loss cls : 2.9769179821014404,loss l2 : 1.99935781955719,lr : 0.001\n",
            "Epoch 44/50:  83%|▊| 500/606 [01:42<00:20,  5.06im2021-03-26 18:32:18,429 train.py[line:353] INFO: Train step_13280: loss : 17.868452072143555,loss xy : 8.500575065612793,loss wh : 0.6609907746315002,loss obj : 8.68494701385498，loss cls : 0.021939989179372787,loss l2 : 2.654038429260254,lr : 0.001\n",
            "Epoch 44/50:  89%|▉| 540/606 [01:50<00:12,  5.09im2021-03-26 18:32:26,303 train.py[line:353] INFO: Train step_13300: loss : 16.60129737854004,loss xy : 6.075623035430908,loss wh : 0.14585530757904053,loss obj : 7.978589057922363，loss cls : 2.4012298583984375,loss l2 : 2.369990348815918,lr : 0.001\n",
            "Epoch 44/50:  96%|▉| 580/606 [01:58<00:05,  5.13im2021-03-26 18:32:34,268 train.py[line:353] INFO: Train step_13320: loss : 11.30086612701416,loss xy : 7.914911270141602,loss wh : 0.1589854657649994,loss obj : 2.926332473754883，loss cls : 0.3006371557712555,loss l2 : 1.2186137437820435,lr : 0.001\n",
            "Epoch 44/50: 100%|█| 606/606 [02:03<00:00,  5.23im2021-03-26 18:32:39,416 train.py[line:364] INFO: Checkpoint 44 saved !\n",
            "Epoch 44/50: 100%|█| 606/606 [02:03<00:00,  4.89im\n",
            "Epoch 45/50:   2%| | 14/606 [00:04<03:11,  3.09img2021-03-26 18:32:44,772 train.py[line:353] INFO: Train step_13340: loss : 9.954474449157715,loss xy : 6.135860919952393,loss wh : 0.11161670833826065,loss obj : 3.7014853954315186，loss cls : 0.005511333234608173,loss l2 : 1.186401605606079,lr : 0.001\n",
            "Epoch 45/50:   9%| | 54/606 [00:12<01:49,  5.05img2021-03-26 18:32:52,695 train.py[line:353] INFO: Train step_13360: loss : 41.81178665161133,loss xy : 12.296217918395996,loss wh : 4.210053443908691,loss obj : 21.572513580322266，loss cls : 3.733001232147217,loss l2 : 13.408286094665527,lr : 0.001\n",
            "Epoch 45/50:  16%|▏| 94/606 [00:20<01:41,  5.04img2021-03-26 18:33:00,674 train.py[line:353] INFO: Train step_13380: loss : 5.4911017417907715,loss xy : 5.185129165649414,loss wh : 0.01164599135518074,loss obj : 0.29107481241226196，loss cls : 0.0032518557272851467,loss l2 : 0.07182592153549194,lr : 0.001\n",
            "Epoch 45/50:  22%|▏| 134/606 [00:28<01:33,  5.06im2021-03-26 18:33:08,621 train.py[line:353] INFO: Train step_13400: loss : 25.05837631225586,loss xy : 10.461386680603027,loss wh : 1.0433670282363892,loss obj : 13.132830619812012，loss cls : 0.420791894197464,loss l2 : 4.730945110321045,lr : 0.001\n",
            "Epoch 45/50:  29%|▎| 174/606 [00:36<01:26,  5.01im2021-03-26 18:33:16,638 train.py[line:353] INFO: Train step_13420: loss : 29.239580154418945,loss xy : 17.165409088134766,loss wh : 0.8224824070930481,loss obj : 6.961410045623779，loss cls : 4.290277004241943,loss l2 : 5.277562618255615,lr : 0.001\n",
            "Epoch 45/50:  35%|▎| 214/606 [00:44<01:16,  5.11im2021-03-26 18:33:24,597 train.py[line:353] INFO: Train step_13440: loss : 13.303719520568848,loss xy : 7.167130947113037,loss wh : 0.12508554756641388,loss obj : 5.521714687347412，loss cls : 0.4897889792919159,loss l2 : 2.138322591781616,lr : 0.001\n",
            "Epoch 45/50:  42%|▍| 254/606 [00:52<01:10,  5.01im2021-03-26 18:33:32,554 train.py[line:353] INFO: Train step_13460: loss : 22.865127563476562,loss xy : 13.094022750854492,loss wh : 0.9168704152107239,loss obj : 8.668682098388672，loss cls : 0.1855517476797104,loss l2 : 4.6179327964782715,lr : 0.001\n",
            "Epoch 45/50:  49%|▍| 294/606 [01:00<01:03,  4.90im2021-03-26 18:33:40,579 train.py[line:353] INFO: Train step_13480: loss : 18.64865493774414,loss xy : 13.113539695739746,loss wh : 0.18505582213401794,loss obj : 4.661715984344482，loss cls : 0.6883429884910583,loss l2 : 2.1415956020355225,lr : 0.001\n",
            "Epoch 45/50:  55%|▌| 334/606 [01:08<00:54,  5.03im2021-03-26 18:33:48,554 train.py[line:353] INFO: Train step_13500: loss : 31.855491638183594,loss xy : 10.591560363769531,loss wh : 1.3863664865493774,loss obj : 19.598215103149414，loss cls : 0.27935025095939636,loss l2 : 7.109863758087158,lr : 0.001\n",
            "Epoch 45/50:  62%|▌| 374/606 [01:16<00:45,  5.08im2021-03-26 18:33:56,521 train.py[line:353] INFO: Train step_13520: loss : 26.821773529052734,loss xy : 22.099231719970703,loss wh : 0.7278720140457153,loss obj : 3.9532411098480225，loss cls : 0.04142845794558525,loss l2 : 2.471085548400879,lr : 0.001\n",
            "Epoch 45/50:  68%|▋| 414/606 [01:24<00:38,  4.98im2021-03-26 18:34:04,480 train.py[line:353] INFO: Train step_13540: loss : 19.126359939575195,loss xy : 15.600385665893555,loss wh : 0.5676965713500977,loss obj : 2.9387197494506836，loss cls : 0.019556859508156776,loss l2 : 2.185778856277466,lr : 0.001\n",
            "Epoch 45/50:  75%|▋| 454/606 [01:32<00:29,  5.08im2021-03-26 18:34:12,414 train.py[line:353] INFO: Train step_13560: loss : 23.345977783203125,loss xy : 15.07586669921875,loss wh : 0.7078791856765747,loss obj : 6.756033897399902，loss cls : 0.806198239326477,loss l2 : 3.681907892227173,lr : 0.001\n",
            "Epoch 45/50:  82%|▊| 494/606 [01:40<00:21,  5.11im2021-03-26 18:34:20,295 train.py[line:353] INFO: Train step_13580: loss : 31.803619384765625,loss xy : 16.941970825195312,loss wh : 0.42335593700408936,loss obj : 14.261513710021973，loss cls : 0.17677998542785645,loss l2 : 4.214663505554199,lr : 0.001\n",
            "Epoch 45/50:  88%|▉| 534/606 [01:48<00:14,  5.08im2021-03-26 18:34:28,247 train.py[line:353] INFO: Train step_13600: loss : 11.931241035461426,loss xy : 6.5332489013671875,loss wh : 0.32875850796699524,loss obj : 5.062570571899414，loss cls : 0.006663635838776827,loss l2 : 2.1736109256744385,lr : 0.001\n",
            "Epoch 45/50:  95%|▉| 574/606 [01:56<00:06,  4.98im2021-03-26 18:34:36,311 train.py[line:353] INFO: Train step_13620: loss : 42.8792610168457,loss xy : 21.393447875976562,loss wh : 1.8616104125976562,loss obj : 17.904260635375977，loss cls : 1.719942569732666,loss l2 : 9.084217071533203,lr : 0.001\n",
            "Epoch 45/50: 100%|█| 606/606 [02:02<00:00,  5.21im2021-03-26 18:34:42,666 train.py[line:364] INFO: Checkpoint 45 saved !\n",
            "Epoch 45/50: 100%|█| 606/606 [02:03<00:00,  4.92im\n",
            "Epoch 46/50:   1%| | 8/606 [00:04<06:02,  1.65img/2021-03-26 18:34:47,246 train.py[line:353] INFO: Train step_13640: loss : 9.579764366149902,loss xy : 8.078916549682617,loss wh : 0.06259389221668243,loss obj : 1.4325212240219116，loss cls : 0.005733654834330082,loss l2 : 0.29549145698547363,lr : 0.001\n",
            "Epoch 46/50:   8%| | 48/606 [00:12<01:53,  4.90img2021-03-26 18:34:55,289 train.py[line:353] INFO: Train step_13660: loss : 4.400200366973877,loss xy : 2.46142578125,loss wh : 0.01065814308822155,loss obj : 1.925111174583435，loss cls : 0.0030055097304284573,loss l2 : 0.6690130829811096,lr : 0.001\n",
            "Epoch 46/50:  15%|▏| 88/606 [00:20<01:42,  5.03img2021-03-26 18:35:03,294 train.py[line:353] INFO: Train step_13680: loss : 12.874956130981445,loss xy : 9.51693344116211,loss wh : 0.15502920746803284,loss obj : 3.1913771629333496，loss cls : 0.011615649797022343,loss l2 : 1.2808678150177002,lr : 0.001\n",
            "Epoch 46/50:  21%|▏| 128/606 [00:28<01:36,  4.97im2021-03-26 18:35:11,295 train.py[line:353] INFO: Train step_13700: loss : 41.35825729370117,loss xy : 16.899639129638672,loss wh : 1.0922654867172241,loss obj : 19.599498748779297，loss cls : 3.7668540477752686,loss l2 : 9.069412231445312,lr : 0.001\n",
            "Epoch 46/50:  28%|▎| 168/606 [00:36<01:26,  5.06im2021-03-26 18:35:19,245 train.py[line:353] INFO: Train step_13720: loss : 14.107941627502441,loss xy : 7.890931129455566,loss wh : 0.11747930943965912,loss obj : 6.096842288970947，loss cls : 0.0026881753001362085,loss l2 : 1.592616081237793,lr : 0.001\n",
            "Epoch 46/50:  34%|▎| 208/606 [00:44<01:19,  5.03im2021-03-26 18:35:27,208 train.py[line:353] INFO: Train step_13740: loss : 32.47261047363281,loss xy : 15.597513198852539,loss wh : 1.0166029930114746,loss obj : 15.284698486328125，loss cls : 0.5737966299057007,loss l2 : 6.69533634185791,lr : 0.001\n",
            "Epoch 46/50:  41%|▍| 248/606 [00:52<01:10,  5.09im2021-03-26 18:35:35,171 train.py[line:353] INFO: Train step_13760: loss : 24.94930076599121,loss xy : 11.722511291503906,loss wh : 0.29116544127464294,loss obj : 8.207670211791992，loss cls : 4.72795295715332,loss l2 : 3.4615957736968994,lr : 0.001\n",
            "Epoch 46/50:  48%|▍| 288/606 [01:00<01:04,  4.97im2021-03-26 18:35:43,212 train.py[line:353] INFO: Train step_13780: loss : 11.542535781860352,loss xy : 7.089168548583984,loss wh : 0.0609838142991066,loss obj : 4.239253997802734，loss cls : 0.15312935411930084,loss l2 : 1.5168038606643677,lr : 0.001\n",
            "Epoch 46/50:  54%|▌| 328/606 [01:08<00:55,  5.02im2021-03-26 18:35:51,178 train.py[line:353] INFO: Train step_13800: loss : 33.123291015625,loss xy : 15.610148429870605,loss wh : 0.7502015233039856,loss obj : 14.562270164489746，loss cls : 2.200672149658203,loss l2 : 6.157407760620117,lr : 0.001\n",
            "Epoch 46/50:  61%|▌| 368/606 [01:16<00:48,  4.92im2021-03-26 18:35:59,243 train.py[line:353] INFO: Train step_13820: loss : 23.170795440673828,loss xy : 17.470855712890625,loss wh : 0.24858005344867706,loss obj : 3.519547939300537，loss cls : 1.931809902191162,loss l2 : 2.385835886001587,lr : 0.001\n",
            "Epoch 46/50:  67%|▋| 408/606 [01:24<00:39,  5.01im2021-03-26 18:36:07,176 train.py[line:353] INFO: Train step_13840: loss : 36.12269592285156,loss xy : 18.578012466430664,loss wh : 1.0578373670578003,loss obj : 16.400169372558594，loss cls : 0.08667778223752975,loss l2 : 7.695175647735596,lr : 0.001\n",
            "Epoch 46/50:  74%|▋| 448/606 [01:32<00:31,  5.06im2021-03-26 18:36:15,150 train.py[line:353] INFO: Train step_13860: loss : 28.22026252746582,loss xy : 8.32944393157959,loss wh : 0.28632214665412903,loss obj : 14.270276069641113，loss cls : 5.334218978881836,loss l2 : 4.718168258666992,lr : 0.001\n",
            "Epoch 46/50:  81%|▊| 488/606 [01:40<00:23,  4.98im2021-03-26 18:36:23,143 train.py[line:353] INFO: Train step_13880: loss : 7.6894049644470215,loss xy : 6.341015815734863,loss wh : 0.25128230452537537,loss obj : 1.0424201488494873，loss cls : 0.05468723922967911,loss l2 : 0.745293915271759,lr : 0.001\n",
            "Epoch 46/50:  87%|▊| 528/606 [01:48<00:15,  4.92im2021-03-26 18:36:31,228 train.py[line:353] INFO: Train step_13900: loss : 49.65468978881836,loss xy : 19.353099822998047,loss wh : 6.701773166656494,loss obj : 18.715131759643555，loss cls : 4.884685516357422,loss l2 : 17.7785701751709,lr : 0.001\n",
            "Epoch 46/50:  94%|▉| 568/606 [01:56<00:07,  5.02im2021-03-26 18:36:39,211 train.py[line:353] INFO: Train step_13920: loss : 18.32985496520996,loss xy : 12.07736873626709,loss wh : 0.23235410451889038,loss obj : 5.616166591644287，loss cls : 0.4039642810821533,loss l2 : 2.4052743911743164,lr : 0.001\n",
            "Epoch 46/50: 100%|█| 606/606 [02:03<00:00,  5.19im2021-03-26 18:36:46,724 train.py[line:364] INFO: Checkpoint 46 saved !\n",
            "Epoch 46/50: 100%|█| 606/606 [02:04<00:00,  4.88im\n",
            "Epoch 47/50:   0%| | 2/606 [00:02<13:14,  1.32s/im2021-03-26 18:36:49,971 train.py[line:353] INFO: Train step_13940: loss : 16.002365112304688,loss xy : 8.836395263671875,loss wh : 0.3172319829463959,loss obj : 5.923575401306152，loss cls : 0.9251617193222046,loss l2 : 2.624769926071167,lr : 0.001\n",
            "Epoch 47/50:   7%| | 42/606 [00:10<01:52,  5.01img2021-03-26 18:36:57,908 train.py[line:353] INFO: Train step_13960: loss : 12.61440372467041,loss xy : 10.109158515930176,loss wh : 0.38808250427246094,loss obj : 2.092363119125366，loss cls : 0.024799764156341553,loss l2 : 1.41388738155365,lr : 0.001\n",
            "Epoch 47/50:  14%|▏| 82/606 [00:18<01:44,  5.00img2021-03-26 18:37:05,870 train.py[line:353] INFO: Train step_13980: loss : 5.2163801193237305,loss xy : 4.847187042236328,loss wh : 0.06409118324518204,loss obj : 0.3050432503223419，loss cls : 5.8770943724084646e-05,loss l2 : 0.15286114811897278,lr : 0.001\n",
            "Epoch 47/50:  20%|▏| 122/606 [00:26<01:35,  5.05im2021-03-26 18:37:13,862 train.py[line:353] INFO: Train step_14000: loss : 37.1360969543457,loss xy : 28.00400733947754,loss wh : 0.3319057822227478,loss obj : 8.288189888000488，loss cls : 0.5119919180870056,loss l2 : 3.0746469497680664,lr : 0.001\n",
            "Epoch 47/50:  27%|▎| 162/606 [00:34<01:28,  5.03im2021-03-26 18:37:21,823 train.py[line:353] INFO: Train step_14020: loss : 22.701126098632812,loss xy : 8.309639930725098,loss wh : 0.2881057858467102,loss obj : 13.990026473999023，loss cls : 0.11335349082946777,loss l2 : 3.825749397277832,lr : 0.001\n",
            "Epoch 47/50:  33%|▎| 202/606 [00:42<01:20,  5.05im2021-03-26 18:37:29,798 train.py[line:353] INFO: Train step_14040: loss : 79.77564239501953,loss xy : 25.5460262298584,loss wh : 2.9545023441314697,loss obj : 49.6544075012207，loss cls : 1.6207062005996704,loss l2 : 16.20805549621582,lr : 0.001\n",
            "Epoch 47/50:  40%|▍| 242/606 [00:50<01:12,  5.03im2021-03-26 18:37:37,743 train.py[line:353] INFO: Train step_14060: loss : 11.482660293579102,loss xy : 8.558832168579102,loss wh : 0.39189594984054565,loss obj : 2.5203566551208496，loss cls : 0.011576117016375065,loss l2 : 1.4148484468460083,lr : 0.001\n",
            "Epoch 47/50:  47%|▍| 282/606 [00:58<01:04,  5.02im2021-03-26 18:37:45,745 train.py[line:353] INFO: Train step_14080: loss : 33.161231994628906,loss xy : 18.172992706298828,loss wh : 0.58800208568573,loss obj : 9.800165176391602，loss cls : 4.600073337554932,loss l2 : 5.42022180557251,lr : 0.001\n",
            "Epoch 47/50:  53%|▌| 322/606 [01:06<00:55,  5.08im2021-03-26 18:37:53,700 train.py[line:353] INFO: Train step_14100: loss : 39.47429656982422,loss xy : 19.622867584228516,loss wh : 0.6580438017845154,loss obj : 14.567283630371094，loss cls : 4.626103401184082,loss l2 : 6.456200122833252,lr : 0.001\n",
            "Epoch 47/50:  60%|▌| 362/606 [01:14<00:48,  5.02im2021-03-26 18:38:01,647 train.py[line:353] INFO: Train step_14120: loss : 9.837249755859375,loss xy : 7.929451942443848,loss wh : 0.2517990171909332,loss obj : 1.2757086753845215，loss cls : 0.38029131293296814,loss l2 : 0.8977384567260742,lr : 0.001\n",
            "Epoch 47/50:  66%|▋| 402/606 [01:22<00:41,  4.96im2021-03-26 18:38:09,613 train.py[line:353] INFO: Train step_14140: loss : 20.739028930664062,loss xy : 11.671441078186035,loss wh : 0.14276227355003357,loss obj : 8.790241241455078，loss cls : 0.13458356261253357,loss l2 : 2.658203125,lr : 0.001\n",
            "Epoch 47/50:  73%|▋| 442/606 [01:30<00:32,  5.03im2021-03-26 18:38:17,611 train.py[line:353] INFO: Train step_14160: loss : 21.199953079223633,loss xy : 12.687829971313477,loss wh : 0.4284389913082123,loss obj : 7.99304723739624，loss cls : 0.09063639491796494,loss l2 : 3.872270345687866,lr : 0.001\n",
            "Epoch 47/50:  80%|▊| 482/606 [01:38<00:24,  5.01im2021-03-26 18:38:25,571 train.py[line:353] INFO: Train step_14180: loss : 25.603506088256836,loss xy : 15.018020629882812,loss wh : 0.49408894777297974,loss obj : 9.738727569580078，loss cls : 0.3526686429977417,loss l2 : 3.5014731884002686,lr : 0.001\n",
            "Epoch 47/50:  86%|▊| 522/606 [01:46<00:16,  4.98im2021-03-26 18:38:33,566 train.py[line:353] INFO: Train step_14200: loss : 17.63512420654297,loss xy : 11.159536361694336,loss wh : 0.37435004115104675,loss obj : 5.299044609069824，loss cls : 0.8021934628486633,loss l2 : 2.3926761150360107,lr : 0.001\n",
            "Epoch 47/50:  93%|▉| 562/606 [01:54<00:08,  5.06im2021-03-26 18:38:41,539 train.py[line:353] INFO: Train step_14220: loss : 18.831296920776367,loss xy : 11.613243103027344,loss wh : 0.3502209782600403,loss obj : 6.3197784423828125，loss cls : 0.5480557084083557,loss l2 : 3.0167791843414307,lr : 0.001\n",
            "Epoch 47/50:  99%|▉| 602/606 [02:02<00:00,  5.21im2021-03-26 18:38:49,281 train.py[line:353] INFO: Train step_14240: loss : 5.607443332672119,loss xy : 3.8409554958343506,loss wh : 0.08107222616672516,loss obj : 1.0002492666244507，loss cls : 0.6851662397384644,loss l2 : 0.6267568469047546,lr : 0.001\n",
            "Epoch 47/50: 100%|█| 606/606 [02:02<00:00,  5.22im2021-03-26 18:38:50,161 train.py[line:364] INFO: Checkpoint 47 saved !\n",
            "Epoch 47/50: 100%|█| 606/606 [02:03<00:00,  4.91im\n",
            "Epoch 48/50:   6%| | 36/606 [00:09<01:54,  4.99img2021-03-26 18:39:00,019 train.py[line:353] INFO: Train step_14260: loss : 17.326004028320312,loss xy : 12.441604614257812,loss wh : 0.11372488737106323,loss obj : 2.747973918914795，loss cls : 2.0227012634277344,loss l2 : 1.4754000902175903,lr : 0.001\n",
            "Epoch 48/50:  13%|▏| 76/606 [00:17<01:45,  5.02img2021-03-26 18:39:07,985 train.py[line:353] INFO: Train step_14280: loss : 24.255956649780273,loss xy : 11.588725090026855,loss wh : 0.3286052942276001,loss obj : 12.04908275604248，loss cls : 0.2895423173904419,loss l2 : 3.9750025272369385,lr : 0.001\n",
            "Epoch 48/50:  19%|▏| 116/606 [00:25<01:37,  5.00im2021-03-26 18:39:16,028 train.py[line:353] INFO: Train step_14300: loss : 7.788904666900635,loss xy : 6.668046951293945,loss wh : 0.1086152195930481,loss obj : 0.9519873261451721，loss cls : 0.06025508791208267,loss l2 : 0.6370763182640076,lr : 0.001\n",
            "Epoch 48/50:  26%|▎| 156/606 [00:33<01:30,  4.97im2021-03-26 18:39:24,045 train.py[line:353] INFO: Train step_14320: loss : 11.484427452087402,loss xy : 8.766578674316406,loss wh : 0.1051470935344696,loss obj : 2.518710136413574，loss cls : 0.09399086982011795,loss l2 : 1.0328718423843384,lr : 0.001\n",
            "Epoch 48/50:  32%|▎| 196/606 [00:41<01:20,  5.07im2021-03-26 18:39:32,023 train.py[line:353] INFO: Train step_14340: loss : 11.708186149597168,loss xy : 7.857902526855469,loss wh : 0.43672502040863037,loss obj : 2.669173240661621，loss cls : 0.7443860173225403,loss l2 : 1.8627461194992065,lr : 0.001\n",
            "Epoch 48/50:  39%|▍| 236/606 [00:49<01:14,  5.00im2021-03-26 18:39:39,953 train.py[line:353] INFO: Train step_14360: loss : 22.004430770874023,loss xy : 14.963174819946289,loss wh : 0.7217719554901123,loss obj : 6.273704528808594，loss cls : 0.04577833414077759,loss l2 : 3.5034825801849365,lr : 0.001\n",
            "Epoch 48/50:  46%|▍| 276/606 [00:57<01:05,  5.03im2021-03-26 18:39:47,987 train.py[line:353] INFO: Train step_14380: loss : 41.08932113647461,loss xy : 19.369773864746094,loss wh : 2.192476749420166,loss obj : 17.914426803588867，loss cls : 1.612645149230957,loss l2 : 9.203752517700195,lr : 0.001\n",
            "Epoch 48/50:  52%|▌| 316/606 [01:05<00:57,  5.03im2021-03-26 18:39:55,937 train.py[line:353] INFO: Train step_14400: loss : 15.364441871643066,loss xy : 9.838024139404297,loss wh : 0.32493001222610474,loss obj : 5.151113986968994，loss cls : 0.050374433398246765,loss l2 : 2.3640050888061523,lr : 0.001\n",
            "Epoch 48/50:  59%|▌| 356/606 [01:13<00:50,  4.98im2021-03-26 18:40:03,912 train.py[line:353] INFO: Train step_14420: loss : 31.015342712402344,loss xy : 21.645679473876953,loss wh : 0.5020408630371094,loss obj : 8.734763145446777，loss cls : 0.13285750150680542,loss l2 : 3.8270366191864014,lr : 0.001\n",
            "Epoch 48/50:  65%|▋| 396/606 [01:21<00:41,  5.07im2021-03-26 18:40:11,848 train.py[line:353] INFO: Train step_14440: loss : 33.88793182373047,loss xy : 17.183338165283203,loss wh : 0.7559864521026611,loss obj : 14.594571113586426，loss cls : 1.3540339469909668,loss l2 : 7.278327941894531,lr : 0.001\n",
            "Epoch 48/50:  72%|▋| 436/606 [01:29<00:33,  5.09im2021-03-26 18:40:19,733 train.py[line:353] INFO: Train step_14460: loss : 12.951623916625977,loss xy : 4.700311660766602,loss wh : 0.04828614369034767,loss obj : 7.06434965133667，loss cls : 1.1386770009994507,loss l2 : 2.4529170989990234,lr : 0.001\n",
            "Epoch 48/50:  79%|▊| 476/606 [01:37<00:25,  5.00im2021-03-26 18:40:27,738 train.py[line:353] INFO: Train step_14480: loss : 21.01102638244629,loss xy : 12.719474792480469,loss wh : 0.225385844707489,loss obj : 7.595306396484375，loss cls : 0.47085967659950256,loss l2 : 3.0687267780303955,lr : 0.001\n",
            "Epoch 48/50:  85%|▊| 516/606 [01:45<00:17,  5.03im2021-03-26 18:40:35,690 train.py[line:353] INFO: Train step_14500: loss : 30.630006790161133,loss xy : 11.676706314086914,loss wh : 1.175537347793579,loss obj : 17.017696380615234，loss cls : 0.7600662708282471,loss l2 : 5.515779495239258,lr : 0.001\n",
            "Epoch 48/50:  92%|▉| 556/606 [01:53<00:10,  4.96im2021-03-26 18:40:43,716 train.py[line:353] INFO: Train step_14520: loss : 5.515410423278809,loss xy : 4.700357913970947,loss wh : 0.026890072971582413,loss obj : 0.7855638861656189，loss cls : 0.002598197665065527,loss l2 : 0.15558668971061707,lr : 0.001\n",
            "Epoch 48/50:  98%|▉| 596/606 [02:00<00:01,  5.22im2021-03-26 18:40:51,495 train.py[line:353] INFO: Train step_14540: loss : 43.618804931640625,loss xy : 17.81553077697754,loss wh : 0.4618232846260071,loss obj : 22.26361656188965，loss cls : 3.0778353214263916,loss l2 : 9.128239631652832,lr : 0.001\n",
            "Epoch 48/50: 100%|█| 606/606 [02:02<00:00,  5.24im2021-03-26 18:40:53,542 train.py[line:364] INFO: Checkpoint 48 saved !\n",
            "Epoch 48/50: 100%|█| 606/606 [02:03<00:00,  4.91im\n",
            "Epoch 49/50:   5%| | 30/606 [00:08<02:00,  4.79img2021-03-26 18:41:02,349 train.py[line:353] INFO: Train step_14560: loss : 22.882843017578125,loss xy : 16.33494758605957,loss wh : 0.3348304331302643,loss obj : 6.1691060066223145，loss cls : 0.04395917057991028,loss l2 : 2.5614047050476074,lr : 0.001\n",
            "Epoch 49/50:  12%| | 70/606 [00:16<01:45,  5.09img2021-03-26 18:41:10,252 train.py[line:353] INFO: Train step_14580: loss : 10.357227325439453,loss xy : 7.050210475921631,loss wh : 0.4756949543952942,loss obj : 2.7282874584198，loss cls : 0.10303428024053574,loss l2 : 1.9786367416381836,lr : 0.001\n",
            "Epoch 49/50:  18%|▏| 110/606 [00:24<01:39,  4.98im2021-03-26 18:41:18,234 train.py[line:353] INFO: Train step_14600: loss : 40.15181350708008,loss xy : 17.593219757080078,loss wh : 0.6615326404571533,loss obj : 21.84922981262207，loss cls : 0.04783109202980995,loss l2 : 7.88108491897583,lr : 0.001\n",
            "Epoch 49/50:  25%|▏| 150/606 [00:32<01:29,  5.10im2021-03-26 18:41:26,143 train.py[line:353] INFO: Train step_14620: loss : 58.580467224121094,loss xy : 27.866870880126953,loss wh : 1.1639196872711182,loss obj : 28.80082893371582，loss cls : 0.7488473653793335,loss l2 : 8.418985366821289,lr : 0.001\n",
            "Epoch 49/50:  31%|▎| 190/606 [00:40<01:22,  5.03im2021-03-26 18:41:34,147 train.py[line:353] INFO: Train step_14640: loss : 30.4576416015625,loss xy : 14.95060920715332,loss wh : 0.43600109219551086,loss obj : 7.055761337280273，loss cls : 8.015270233154297,loss l2 : 5.924034118652344,lr : 0.001\n",
            "Epoch 49/50:  38%|▍| 230/606 [00:48<01:14,  5.02im2021-03-26 18:41:42,144 train.py[line:353] INFO: Train step_14660: loss : 49.62315368652344,loss xy : 19.485931396484375,loss wh : 2.5110435485839844,loss obj : 26.0416259765625，loss cls : 1.5845513343811035,loss l2 : 11.721619606018066,lr : 0.001\n",
            "Epoch 49/50:  45%|▍| 270/606 [00:56<01:07,  5.01im2021-03-26 18:41:50,131 train.py[line:353] INFO: Train step_14680: loss : 43.12588882446289,loss xy : 14.901517868041992,loss wh : 1.695313811302185,loss obj : 20.271678924560547，loss cls : 6.257376194000244,loss l2 : 10.085062980651855,lr : 0.001\n",
            "Epoch 49/50:  51%|▌| 310/606 [01:04<00:57,  5.13im2021-03-26 18:41:57,981 train.py[line:353] INFO: Train step_14700: loss : 17.168498992919922,loss xy : 7.111842155456543,loss wh : 0.13400915265083313,loss obj : 9.543033599853516，loss cls : 0.3796144127845764,loss l2 : 1.8885666131973267,lr : 0.001\n",
            "Epoch 49/50:  58%|▌| 350/606 [01:11<00:49,  5.12im2021-03-26 18:42:05,830 train.py[line:353] INFO: Train step_14720: loss : 8.496609687805176,loss xy : 5.430566310882568,loss wh : 0.34336841106414795,loss obj : 2.6958537101745605，loss cls : 0.026821350678801537,loss l2 : 1.4471311569213867,lr : 0.001\n",
            "Epoch 49/50:  64%|▋| 390/606 [01:19<00:41,  5.17im2021-03-26 18:42:13,645 train.py[line:353] INFO: Train step_14740: loss : 7.356115818023682,loss xy : 6.256202697753906,loss wh : 0.10754774510860443,loss obj : 0.9843473434448242，loss cls : 0.008018247783184052,loss l2 : 0.443251371383667,lr : 0.001\n",
            "Epoch 49/50:  71%|▋| 430/606 [01:27<00:34,  5.11im2021-03-26 18:42:21,562 train.py[line:353] INFO: Train step_14760: loss : 18.510225296020508,loss xy : 9.02499008178711,loss wh : 0.28917911648750305,loss obj : 9.146629333496094，loss cls : 0.04942791908979416,loss l2 : 3.003830671310425,lr : 0.001\n",
            "Epoch 49/50:  78%|▊| 470/606 [01:35<00:27,  5.01im2021-03-26 18:42:29,478 train.py[line:353] INFO: Train step_14780: loss : 44.294677734375,loss xy : 19.633773803710938,loss wh : 2.229189872741699,loss obj : 22.286182403564453，loss cls : 0.14552974700927734,loss l2 : 7.712541103363037,lr : 0.001\n",
            "Epoch 49/50:  84%|▊| 510/606 [01:43<00:18,  5.10im2021-03-26 18:42:37,385 train.py[line:353] INFO: Train step_14800: loss : 11.359264373779297,loss xy : 10.388629913330078,loss wh : 0.222510427236557,loss obj : 0.7421075701713562，loss cls : 0.0060170600190758705,loss l2 : 0.6489885449409485,lr : 0.001\n",
            "Epoch 49/50:  91%|▉| 550/606 [01:51<00:11,  5.09im2021-03-26 18:42:45,288 train.py[line:353] INFO: Train step_14820: loss : 9.204968452453613,loss xy : 5.354117393493652,loss wh : 0.24615558981895447,loss obj : 3.597585439682007，loss cls : 0.007109750062227249,loss l2 : 1.5906795263290405,lr : 0.001\n",
            "Epoch 49/50:  97%|▉| 590/606 [01:59<00:03,  5.19im2021-03-26 18:42:53,092 train.py[line:353] INFO: Train step_14840: loss : 9.23800277709961,loss xy : 7.357138633728027,loss wh : 0.07920609414577484,loss obj : 1.7808570861816406，loss cls : 0.020801981911063194,loss l2 : 0.7324798107147217,lr : 0.001\n",
            "Epoch 49/50: 100%|█| 606/606 [02:02<00:00,  5.23im2021-03-26 18:42:56,321 train.py[line:364] INFO: Checkpoint 49 saved !\n",
            "Epoch 49/50: 100%|█| 606/606 [02:02<00:00,  4.94im\n",
            "Epoch 50/50:   4%| | 24/606 [00:06<02:07,  4.56img2021-03-26 18:43:03,660 train.py[line:353] INFO: Train step_14860: loss : 33.80633544921875,loss xy : 21.30845069885254,loss wh : 0.7572447657585144,loss obj : 11.611979484558105，loss cls : 0.12866194546222687,loss l2 : 5.054340362548828,lr : 0.001\n",
            "Epoch 50/50:  11%| | 64/606 [00:14<01:47,  5.03img2021-03-26 18:43:11,566 train.py[line:353] INFO: Train step_14880: loss : 41.63523864746094,loss xy : 19.4246883392334,loss wh : 2.087409257888794,loss obj : 17.24658203125，loss cls : 2.8765580654144287,loss l2 : 8.220159530639648,lr : 0.001\n",
            "Epoch 50/50:  17%|▏| 104/606 [00:22<01:40,  4.98im2021-03-26 18:43:19,515 train.py[line:353] INFO: Train step_14900: loss : 10.346966743469238,loss xy : 6.36796236038208,loss wh : 0.33479800820350647,loss obj : 2.8932700157165527，loss cls : 0.7509368658065796,loss l2 : 1.8025450706481934,lr : 0.001\n",
            "Epoch 50/50:  24%|▏| 144/606 [00:30<01:31,  5.03im2021-03-26 18:43:27,497 train.py[line:353] INFO: Train step_14920: loss : 15.21750259399414,loss xy : 10.594766616821289,loss wh : 0.39907190203666687,loss obj : 4.218073844909668，loss cls : 0.005590381566435099,loss l2 : 2.150273561477661,lr : 0.001\n",
            "Epoch 50/50:  30%|▎| 184/606 [00:38<01:23,  5.08im2021-03-26 18:43:35,431 train.py[line:353] INFO: Train step_14940: loss : 31.266611099243164,loss xy : 23.89883804321289,loss wh : 1.086058497428894,loss obj : 6.188741207122803，loss cls : 0.09297439455986023,loss l2 : 5.163213729858398,lr : 0.001\n",
            "Epoch 50/50:  37%|▎| 224/606 [00:46<01:15,  5.03im2021-03-26 18:43:43,382 train.py[line:353] INFO: Train step_14960: loss : 31.31585693359375,loss xy : 10.25943660736084,loss wh : 4.664617538452148,loss obj : 14.01621150970459，loss cls : 2.37559175491333,loss l2 : 13.181609153747559,lr : 0.001\n",
            "Epoch 50/50:  44%|▍| 264/606 [00:54<01:08,  4.99im2021-03-26 18:43:51,399 train.py[line:353] INFO: Train step_14980: loss : 18.476348876953125,loss xy : 7.3731536865234375,loss wh : 0.1473960429430008,loss obj : 9.324000358581543，loss cls : 1.6317987442016602,loss l2 : 3.525265693664551,lr : 0.001\n",
            "Epoch 50/50:  50%|▌| 304/606 [01:02<00:59,  5.10im2021-03-26 18:43:59,290 train.py[line:353] INFO: Train step_15000: loss : 11.337121963500977,loss xy : 7.47872257232666,loss wh : 0.045524802058935165,loss obj : 3.795945405960083，loss cls : 0.016929281875491142,loss l2 : 1.066781997680664,lr : 0.001\n",
            "Epoch 50/50:  57%|▌| 344/606 [01:10<00:51,  5.04im2021-03-26 18:44:07,244 train.py[line:353] INFO: Train step_15020: loss : 53.639625549316406,loss xy : 21.52747917175293,loss wh : 1.1672471761703491,loss obj : 28.57320213317871，loss cls : 2.3716952800750732,loss l2 : 9.025391578674316,lr : 0.001\n",
            "Epoch 50/50:  63%|▋| 384/606 [01:18<00:43,  5.09im2021-03-26 18:44:15,138 train.py[line:353] INFO: Train step_15040: loss : 20.772153854370117,loss xy : 11.105720520019531,loss wh : 0.3577211797237396,loss obj : 8.616573333740234，loss cls : 0.6921390295028687,loss l2 : 3.9933159351348877,lr : 0.001\n",
            "Epoch 50/50:  70%|▋| 424/606 [01:26<00:36,  5.05im2021-03-26 18:44:23,091 train.py[line:353] INFO: Train step_15060: loss : 18.037012100219727,loss xy : 10.380840301513672,loss wh : 0.6042739748954773,loss obj : 2.995154857635498，loss cls : 4.056743144989014,loss l2 : 3.6684625148773193,lr : 0.001\n",
            "Epoch 50/50:  77%|▊| 464/606 [01:34<00:28,  5.04im2021-03-26 18:44:30,999 train.py[line:353] INFO: Train step_15080: loss : 15.38685417175293,loss xy : 6.811555862426758,loss wh : 0.45252397656440735,loss obj : 6.947587966918945，loss cls : 1.175186038017273,loss l2 : 2.6492769718170166,lr : 0.001\n",
            "Epoch 50/50:  83%|▊| 504/606 [01:42<00:20,  5.00im2021-03-26 18:44:38,995 train.py[line:353] INFO: Train step_15100: loss : 56.66362762451172,loss xy : 22.424537658691406,loss wh : 2.219916582107544,loss obj : 24.52912139892578，loss cls : 7.490050792694092,loss l2 : 10.848669052124023,lr : 0.001\n",
            "Epoch 50/50:  90%|▉| 544/606 [01:50<00:12,  4.97im2021-03-26 18:44:47,007 train.py[line:353] INFO: Train step_15120: loss : 43.69951248168945,loss xy : 19.289691925048828,loss wh : 0.6641419529914856,loss obj : 22.172195434570312，loss cls : 1.5734812021255493,loss l2 : 6.646632671356201,lr : 0.001\n",
            "Epoch 50/50:  96%|▉| 584/606 [01:58<00:04,  5.20im2021-03-26 18:44:54,856 train.py[line:353] INFO: Train step_15140: loss : 21.980579376220703,loss xy : 12.78427791595459,loss wh : 0.7745383381843567,loss obj : 8.181238174438477，loss cls : 0.24052445590496063,loss l2 : 3.365983724594116,lr : 0.001\n",
            "Epoch 50/50: 100%|█| 606/606 [02:02<00:00,  5.22im2021-03-26 18:44:59,214 train.py[line:364] INFO: Checkpoint 50 saved !\n",
            "Epoch 50/50: 100%|█| 606/606 [02:02<00:00,  4.93im\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CR87FGKijXbI"
      },
      "source": [
        "# Load Trained Weights for Custom Detection\n",
        "\n",
        "You can also use this to load previously saved weights!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5iR0H_DUoOl-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "470fb7e0-561b-4282-a822-525b9b71e7e8"
      },
      "source": [
        "#looking at the weights that our model has saved during training\n",
        "!ls checkpoints"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Yolov4_epoch10.pth  Yolov4_epoch22.pth\tYolov4_epoch34.pth  Yolov4_epoch46.pth\n",
            "Yolov4_epoch11.pth  Yolov4_epoch23.pth\tYolov4_epoch35.pth  Yolov4_epoch47.pth\n",
            "Yolov4_epoch12.pth  Yolov4_epoch24.pth\tYolov4_epoch36.pth  Yolov4_epoch48.pth\n",
            "Yolov4_epoch13.pth  Yolov4_epoch25.pth\tYolov4_epoch37.pth  Yolov4_epoch49.pth\n",
            "Yolov4_epoch14.pth  Yolov4_epoch26.pth\tYolov4_epoch38.pth  Yolov4_epoch4.pth\n",
            "Yolov4_epoch15.pth  Yolov4_epoch27.pth\tYolov4_epoch39.pth  Yolov4_epoch50.pth\n",
            "Yolov4_epoch16.pth  Yolov4_epoch28.pth\tYolov4_epoch3.pth   Yolov4_epoch5.pth\n",
            "Yolov4_epoch17.pth  Yolov4_epoch29.pth\tYolov4_epoch40.pth  Yolov4_epoch6.pth\n",
            "Yolov4_epoch18.pth  Yolov4_epoch2.pth\tYolov4_epoch41.pth  Yolov4_epoch7.pth\n",
            "Yolov4_epoch19.pth  Yolov4_epoch30.pth\tYolov4_epoch42.pth  Yolov4_epoch8.pth\n",
            "Yolov4_epoch1.pth   Yolov4_epoch31.pth\tYolov4_epoch43.pth  Yolov4_epoch9.pth\n",
            "Yolov4_epoch20.pth  Yolov4_epoch32.pth\tYolov4_epoch44.pth\n",
            "Yolov4_epoch21.pth  Yolov4_epoch33.pth\tYolov4_epoch45.pth\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vutw9YXmhvWo"
      },
      "source": [
        "#choose random test image\n",
        "import os\n",
        "\n",
        "test_images = [f for f in os.listdir('test') if f.endswith('.jpg')]\n",
        "import random\n",
        "img_path = \"test/\" + random.choice(test_images);"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "091QOGGihsuV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7acd2f49-deb0-47d1-8004-34b16880f108"
      },
      "source": [
        "%%time\n",
        "##change the epoch here to the one you would like to use for inference\n",
        "!python models.py {num_classes} checkpoints/Yolov4_epoch46.pth {img_path} test/_classes.txt"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "passenger_car: 0.999997\n",
            "save plot results to predictions.jpg\n",
            "CPU times: user 32.2 ms, sys: 7.19 ms, total: 39.4 ms\n",
            "Wall time: 4.53 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Af8tiXQLrGgk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "outputId": "723f5bb9-b279-493b-9f6a-a5945c724685"
      },
      "source": [
        "#visualize inference\n",
        "from IPython.display import Image\n",
        "Image('predictions.jpg')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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T24V1c81dtNbngASTDp706azDLhwwI9ag+yFV2Nhl7ccim3CTL5JwXc2I5bK+GY3Ebn+E9KimspIWEjLwDw3asYWrmZvKO3HStvTry4t9LuXnjSUROhVZBkHr2qeRx1Q/a3VmY15byGYyAdfSrdld38DqiQyTIwz5e0mteDWVuoykLw27k7sCIDn2PUUl3LfPIJJp5GPZ93+FLmv8Q1D+UWK2+3gmCJ45R1jcYNVJtEYukMqeQS3+sPb61TntpHlMqysJO2WzTrfVHCqt4hZOgkFUk46rUHK/uyO58O6ZLpdnJFNcRzFm3KU6YxW0BnpzWJ4bkR7J9kokj3fL7e1beRXRB3irHLNWk0SC1mZQwjJB5BFRMhUkHgjtTXiVzklh9GIpkNtFA7sgbLdSWJ/nVK4tB+KMU+jaDTERlRTClT7RRtFAiuBjuaMn0qfYKCgphYgyaYTk9asGOmeVQKxDtBPWnbSPWn+WadtPrQFiPbnqKaYwKn2mjaD1FFwsVuBTsAip/KT0o8oDpTuFiALShcdqn8sUeXRcLEYTuMqfUVYjEn2SXdtIDLyOveo9pBqdR/oMp/21/rUSS3KTIB9aUik2ml2mqFcaQD2qNoEbqtTBaeFp7C3KZtVB4FKLYVc20uKOZi5UVRAB2oMWKs4oxRcdittI7UhB9Ks7aTbRcViv8x7U4J61NtoxRcLEYWjbUmKNtIZHRin7aNlMQyjFP24o20hna+G2xosY/wBpv51Jqg/dwDHBlWofD5C6RGCM/M386m1IgxwYPPmrXLLdnRHY2AABwKcOR7VEHGcVFcC4JHlyqigemTUDPmaHXgyhbmIOPXFXEaxuxmKXy2PZulc/NFhRtyabHHuXKPhh1FDproWqjjudDJYyINyjcPVOaeiY0i4L/wDPVByPrWLDfXlrIVRy2OuK27XUReaRO06hdkqbscZ60JSiOU4yRgXUTLOXRCF9qtW+oXluoIzJHjJU81prDbXAzBOp/wBlqY9rJAc7CAe/UUuZN+8VyWXusZDe2V6Np/cSnsehqUWYhkR3jEkIOSB901RktIpCzMuCehHamWUt9E6pbsZGZtvlN1pqLWsWJydrTR3ujixFqRYReUmfmX0NaYzjrWXoEU6WshubX7PIzcrkHPvWtsPauuDfKrnFO3M7ACadmm7WHaj5h2NUTceDTgRUQb2ozRYdyaimA07d7Uh3FoxSbqM0ABFJil5pM0wF20myl5paAG7TQFPrTs0cUAJilxS0ZoATbRinUuBQBGVFTBf9Akx/z0X+tMxUoB+xOP8Apov8jUy2GivRmlK0mMVRNxc0uajLYphc9qdhcxPkUm4VDvNOBzTsLmJgc0tMWn1Nh3DFJimyTxQjMkir9TQtzAwBEqYPvSuVqOxRtpwKnowP40uBTEM20Yp/FGBQAzApDT8CjavUuo+ppNpbjSb2I8UlQXeq6bYcTzMzf3VFVIfFWnzRylFWMptxvPLZNZurHoWqcj0bw7HnSYyemW/nUuqqqi32jP70VV8JXv2/w/FdKRsdnxtPHDEf0q1qeR9m4wPOHWsW76mqVtC/HkkkjGKR1UyfMT061OuGA5BqG4VeOMEdqkD5v/s5yuVKSe6mq0lgyNkxMD64qlIt7ZwRzeZ8j/dINSxa3eRqC3zL70nGSNlVT3Hi2CuWAyTVyOP/AIkt0CODImf1qNNatpeJ7dR7jitBHtZ9InMDEKZU3Z7daalLZikoNXRy0oaGc+XlQOmKv2+o30QXyzvXGSprSOnhx+7eNx6ZqNrKSE58or26UuaL3HyNL3WMTVrWY7bqAxP/AHlGKtwxI0izWk6MynI5waz5IAxJZQST3qKwsRLdRQuzQln5kU8Yp8q+yw5px+JHoej3k93A/wBpQB0OMgYzWmCKo6Xp4063MYnknBOdz4/pV/j0rrje2pwTacm47Dhg+lLgelMBHvShh60xXHbFPakMantQXXONwBppf/aoC4eSPWjyvelDMe9Ll/amBEUZaVTT8uPSk5PUUCFGKXAPem4FKAPUigdx20U4DkDpTf8AgVKBnvQFxWRR0YNSbBRgg85qWGETSqhkWMHqznAFLYe5FtxSEVJchLaUIJPMJbblQcUbTszjnNJSTG1YYFPrRj1o2mkNUTcU4FP3f6DIQOki/wAjUWM1OE/4l8n/AF0X+RpS2BMrbyaTBPan7DTgpFWRdkJQ+lHle1WMGnw209xL5cERdsZPYD8aHJJXYJNuyKvlGgqF6kCtyLw9OzlbiYJ7Rj+taVtpFpa/OsQLf3m5NYSxC6GsaDe5y9ta3V2cW9u7f7TDaPzNTXmgX0di80t4sTjosS5H4k11lxdQ2NsJZMhBgDA7muf1/wARQrod7OkLMIED4PG7msZVZyNo04xPFNau7j7SU+3tMcncFBXFZy3t2uNtzJ7fOam1S6hvr+SeC3MQkOShPeq2zb1HNCQ2y1HrWpRY23T8epq5H4s1ZMDzlP1FZbRlB8w4NRkr0PJNOwrnTR+OdSQYaONj+NWYvH1wufNtweOxrkSmADg49cUwj5c59qd33Cy7Hcw+NJ7lyqRMoYEDIGB+tSyaZfX6/aXncIBuG1sdK4W3jeW4SNM7nYKMV65BB9l0gQ5zsixz9KzkzoowUkee3Nlczgb5nJ6fezUUemSLuUhvXjvXoWn+EbPULO3kaSZJpsnKngckU7TvBKXkLEX0iyedJGvAx8hxk0ag1C52Hw+QweC7FW4wX4/4Ea2NSIk+zDPWYcVD4ZtFtvDtnDvD4U/MO/J5qa/UeZagDjzRTMnuawULgLileISId3XHWmhQnPP400OdxwGNIR8vXPm3dvHCSqonTAqGCKeA7SQ0Z7VopeaYw5DL9DUo/s+T7twR9RS55dTfkpvoUVjgkv2kli/ckD5R9K0lhtRo8624KK8iBuenFJ9lgb7l0h+tT/ZfK0mdd6tulTGD9aanfQiVOKV0zmrwPY3skUU7Mqnhs9auWt7qTbRCxkyM4q22lyN96IN9MUi2k1uQUR0I4GBS5ovcrklbRjTqkytsurLJHXC05L3Tpjgl4m9DTvMnSVpDyzYzkdcVFp4NrfQNOqSQJL5hG3nNFo9B2qI7jwtCr2cwaRmXeMHkY4roWgQMABkeu72rOsdd0+4Q+VOkZzyrjaTVr7XBI2fMhPXo/tSUpLZkyim9UWRaxsWAOMe9Y+pX66fdJE0TFWBOc81rm6TBCvGw6dfauV8RSKdQjxzhTnDZ71SqSvuQ6cLbGnBe2t3OqIfm2nhhznirhTH8IxXFxXTwXKvGcMORmri6ldAbxMwLNkjORWqqdzGVHsdUF7ijbg5xzS2oEtukhGC6hiB71NtUHBIrXmMrEBXPak2fWrPl+1OWL5hx3o5hcpVCUbavTRLs+UAHIqPyx6UKdxyhYrY9qUA+lWdg9BSrCWYKByTinzCsyAVd08A3Bzj7h61W8Twy+H4XZU84o6qSRtU5GeK6PTbO3uPCem3v2i3t5pwZHkBHIIPy5PXH9KzlNW0NIwd9THvn/dbUILM6qAOScnpU8GkPMAvmoGxu9QK4bw5Of+FriJ7hkjN/KAc5AOWwMdOeleuzD/SkAj2IsWACMZ5HNZqTWiNHFM4u8ENnJcJJKP3ADSHaeAf51MthJJOkEah3dPMUDuvr+tWdWu5Yru7Uaa0yRop3BxmXOMgD2rEj15NL8eeZLC217YIM/wAAKjj88VSnIlwRoT2E1sAZYioPANMK4tcf9NB/I1s63cI9lBKm51duNoz2rGkdzYrII8HzRhWOM8Gm6qtqHs30Gbfal2E9BUAe+f8A5Z28XqWk3Y/Kqa3E0m7zdTSFQDkxR9e/U+1N14olUJPc09lavh+SNpZ9jK5AGdpziudtptFS6QNfvdSbCW3TcBcZOQOO9dL4f+wvY/aNNgjjglG9dg+9nvWEsRze7bc1jQ5dbk97fSx6pFAmAjY3etaWweVWJfKza3b4Bxxk/jW2DgBSallGPrhY6eFYDh1/nXH+IiB4T1b3hH/oQrutTtnu7cxx43ZB5rk9c0O4utCvLVXUNMoUH/gQNNPQGeKwqrSDPFOuMDPrmurb4fahAvnC6iO3nGKq3ngy/WF5jJGQgywzV8yI5Wc5K+UqsSSc96vNFbkbftK/kaja1jB4uYj+dO4KLNRY/N0dBjnbxWEwKkg9a6O1kgjtY4mnjJAx1qhfWkckge3kjxjkbu9Smb1EmlY0PB+n/atVWZ1/dxDI+tei3H/HrJ/umuf8LXFvaaHFHJCySBmLSKpYPk+ordldZbGSRCdpQ4yMVEndnRSVom14d3Cz0xcDDKTnHuas+HgRCued1zcfh8xri7TxVqGnww28QjMcYwuV565/rS2Xi69s4GjWJCDK77ieQWOT/Wq5kYujK7PT/D2f7As8f88xS6jlXtfeUdaq+F5S3huydjncmcfjVnUW3vaqDg+aKZi9zSfecCpVztGRz60xYzjDEk+tPC7VxnNAj5Wvre1i0+3kiz5rAbufaq1slvKuxy6ydsdDV8Wu4fMhP4U4WYBz5XP0oc0aexfcgWzR70wrK6pgfMa14rQ2+kXCicvukTk9uKpi1PaM/lV6CFo9HuRtIzIn9aOZPoEqbSvcwZby6tpmjWckDuO9WYtRv3KlJeCM4NSvYiRtzxEn1xT1sygGyNhx2FTePYvklbcE1G/LMuI329elOh1CaYKTaI259nHrQbaQEsA4J6mmW1k9rdJOkbMVYNtYZBo9wXLNdS3dadf3EsKQWpRpOFGOGOM8GoH0HWLcEvbbccnIrsrTxrcQrtm0OzcD7mC0e38Oa1U8c6PdQlbjw9kkYYebwfWqS00Ik3fVnmD6ZqsaxlraZUb7hCnmlht71pGiLCIx8YfOea9ZTxhouxV/sKRVHCqJe1cXrOpx6pqz3Fnp4t48bdqnc31JoaYlLzMBrO6RGHnIWAJ79PSpQ0yadC0ucoxDAZHpWraWyzTyicNtWB3yPUdK3PC3hi58RwXCRSQx+Xgt5gJyGH/1qEgcuxW0nT7q8Cqk8AIAKo7kE8nvSa1pWo2SxvIFC7i3yOTnHOP0rW8Q+Hr7wjpjam19D+4ZEGxMklj2zWWPFCeeC2vQzAqWCNbnB46VSimr3Icne1ixZQ3+o6aLqJAiAfMjSMGpqQXqgjec5yQJXGKpL4o8+Il9VtLYSg7o1hbjBI/pWjBqMU0CzI4nUrgOuV3fgaiUWtncuDT30KEOpXsmqzWSs4ki+YkyNjHQVs29/frv37nUcZMmOfyqpIyR6mxhThSN7k/McgH06c1Y+1hUIw+WyM5zUtSNEody2b68kmhiVdruSF/eA7jgnnjjpUdteXjaukBn2sjLvKyDjkcjI5NXtPtLl1ivI4HkUMCrdR3B/nXKeKVit9ft7m0uMXouVMqSH/VYYYB9uaEmS7J6Ha/Ey6FxbXUEd22+KeIGNvlC5QnIPQ5rX0nyLfwLoskmmlmWMkxhwCSerfj1rlviTrtjfaLL5U9q4a4j2vEDmTAIOfoc1s6RqttF4A0lYdXCyxwfPIcAxhj05HboPpWhmcP4PeOb4oSTiHz3a7mKRHpj5juP0r2KfzPtUTS7VLQgrGBjYOOK8K8H39vD8S4LhrloohcyuHUHn73H0NewT6zp0EouJb7cHX5mborcEgUgKGqpq5urtraWAIVXyFeLOD/Fn1rg9fkm/wCE1USbc7E3YHfaK6bU59Ov5bu6j1maNJQqhkmKhCO6+lcZrt7aN4uWdbsuqog3Ek5woyTTQmdeGv7nSFtre5EA3H5tu7jHSmtYuugGz86R5RKMy7cE8E8VHo2pWtzCEhnV2UkkA4p1lfyXLXqsFCw33lLg9Rs9fxqJK7NIvQwk8PXU13Ekt7dKJGI2gk+45HHWrNz4AtUCOz3MeCm8TMGDZYAjI78112nMPt0Qb7uRn5vap/GGr/2L4fe6hjWSTzUTDHjk1FuxTOetdC0qylcwW0SsFKk+2K6zRII4bJEiXagUBVHQDnpWErI53AHlcnn2re0Al4rhQR8qrj9aLNk3sXVQ+byv41KY3Lj5CaPPSN0hklAmbkL3Iq/HGxc5bHH5VVibme8EhJ+QjHWqF1p88kLIoAyc8mtm7mECiWSTYgPLVAtzBfblt50fGPunpRYLnNy6JeeU7Ps2KMnmsh/Dl5NBJDuQBgSeexrt7vItZFBJ+XGKxxc/vDvbauAGxQB5HJ8LNcD/ACzWhycDDnn9Krv8M/ESthIYJAOpEoH869Yj1vTBcrm9QkcdauvMkKyyNIEiVT8zdvequxWPGX+HniJQubaPn0lFQp4B153H+jJt7nzB0717CmoafdgRLqEbSHgKp5+lI+yGUKhywGCPSjmYWOWXQ9VtdM8q2hClUG0bsVLOklpYeXdEeaUwfckV1dxOPs8kjyCNVGfwxXEX1y17dGTd8o4APYVKVzdVmZP2Vyc5WnCyfHJWrRGDxVW91WKxMasN8jn5U9R3quVA6sj1rwrCqeHbIMckJ/WptRAF1Z46GUUnh6QTaHbTBAiuCwUdF5PFWbi1W4eNiWBjfev1pmLd9TQPFV54TJlg7jjoDTUnZmIJAFKZnxxgUCPnBbi5lhlBfGFJBUc1iyXt2v8Ay8Sf99VvRAeVP/1zNc/cLhTxTkkmFPWLZe03ULsSMd5k7YfmtVZ5pdOvfNyGWRBjpjg1S8JizOot9tTfFtPGO9aV/cQebqCRkbXkRgPwNKwdjF3S5/1sn/fRqtcXE6EYmf8A76NXDzVG65Ix61COqaVi44vZLLzQ7+VGQSS2PatXTHm862+diCc7SevNZ5uQ2mQwbn3BWXaPu8kcn34rQ02QQS28rciNgSuPerj5nPO1tO53IE5GWtoz/wABpuJQ2RbR5IH8IqxF4os5F3LDLz6gCpl8QW56QP8ApSsPm8iqVuMjFvH0/u0kYnBCi3jAH+yKuf27EekB/OmtriINy26g+7UWFzeRkXNzNbatbq0KBpUKAbevIrQtZ9Qt7qbyMxsQu7Zx9Kj1v7edRstSTRLa4hSOExv5kjSAyAHlVccFsgZXkipptS12PURDbeHoJrq4VioWYlcIcNlw+0YPB5GCQO4rf6vLb9Geas0pNc2ltftRW2/XoS3E2oXHyzjzFJ6PyM1geI9Lu77T1jitog6vu4wvY9614/EGr3Ucstvo9k0Nuyiac3Q8kE4wFfzNpPI6E4yM9a1bK71G+01p20q3jkMavCDNtyWcKvDNkA/NjPXHFCoSe35Mc80pQ1norpfFHrbz8797HlEfg3WbiTEcKkrw2XHfmu+0Pwwlto0MV6pW4XO4JICOTWgt/q4kRDpsQLo0gO7ChV+9uOcLg8EHBB6043GuNknTYywAJXeN2SpbbjOd2Bnb1x25qfZVOzNXjMMt6kfvRVtvCq+cZLm637scKNvTgfoKv/8ACMWG3GZP++qjgn1u7hSWKxh8ooHR942456c9eDkdR3rYtFvJQBIE34yQtS4S0VtzRYii1KSkrR3129SaxWPT9NWziQlFJOS57mue1fwlbavdefNcSo+c/KB6g/0rcvbO7lgeO3/1iuoJP1BP6VOIZiu8qAuM5yP89qPZT7E/XMP/AM/F9667fecLe/D61urdbZNT2MG6mPnPJx+tWLjwLcr4dksoLiOUbY13f7pz0rpJvtaoBDHE3zZOTjv61bQvtyVwD1waix0nl2m+CNS07WUvZcGMMxKhSMggj09629X0m+ufDjWkMJMryHA6dhXo1uLSVdrEhvRj/Kql1p14L9WgVWt9h4LY5pWfcNDyYeFtXbwy1l9m/fGQHaT/ALWaZceDNYlv2dLfEfl4DE99uP516lcW9wkimSErt6EHimZk9vzp2YjzC18G6ysJSWFlyf4W9q17Xw5qNv4ZubFlImmvFaPntjrXdB39qSRzsi6Z8z+hpcr7juec/wDCH6xEdzXDAE9s5/Cg+D9W1GA5vGI6lJA3UfWvSDIT1NG4jvRyeY3M4PS/Cer2E8kk1zmPyXQDcepHFej+GYnt4nEgORGik+pGapP+8Uq3IPUVq6MC3mjORx1otZCbuQ6oAPEtn2+Vf610UUqOxI5GOaoahp0NxeR3zSFTEOV7YFVdHn36UH53M7kZ9MnFDES6+d+jSkYxuX+dZnhTaJbkEckLg/nV6/ge40eRF5kZhhc+9V/DdtJaS3AnUrkLgn8afQDT1RdtuxAI+Uk+9cpIgNswYEM+c/Sux1FQ9m4Y/wAJGa5S6jZHeTcSEGAuPapA85Vf9IjwBnevX613OtyE6NclgQxiIx2rilt7gzoRDIPnB+6eOa7XUUe50i7GCz+WdoHJJq5COO0bjW7M+kwrpPE9xd2GnfarYFX8xVyRkHNY2laVfJqttJJayrGsmSSMAVu+OF/4kgTef9agwDxSkVE5631O+vtL1Ga8fEdnCZtqrjcc9K5JfF4B+a1OfZq6OxyPDHiPhh/ofc+9cDd6Zd2Ntaz3MZRLpN8ZPdc9acdgk7PQ7HR9XTVxfkRNELS1a4POd2CBj9a5SO8ku9WimkDOzSD5QOoz0FbvgZQ0fiAY6aVL29xW18M/DXmvJrN2mEjOy3DD7x/iYfTp+Jpi1Z6V4a1ECxFkbe4jeAsDuTgjccYNbRuF55cfQUafF88pxwcH9TVmdWA4wBSAy3mQnnPtxVqEmUAtnFWUiUMCyjP0qbyx6D8qAPB18G66qOBaqSy4++OKov8ADrxBIuBFCD7yCtdtT1Dq+oTZ/wB/FQPqspOHvZm9g5NNpvcE1FWQzTvhzrVmxkae0ViP754/SuY1W2NhqM1s8qTOjYZ4+QTXRSXkki4DzsPdiKqCNQciJAffmmkxNk2laJY3dhHPNqKRyEcx+WSRWkPCulllJ1NeRniP/wCvWaHkA4bH+6MUp3N1JP1NFh3NWXQtJgkG28LKBn7o6+n86jSxgBzFG7qO6is8Ag5Bx9KerOBgO350WFc3LezhlUAwSK/qzACtGPRYtuWu1X2XBxXKiSQfxt+dLukPVmP1NFgudV/YdoxO7UG/76Ao/sHS/wCK6dj/ANdBXK5PcmjJ9TRYLnU+MtVh0fSLKK1WGUEQRzJKNwkRBvVTzyOeehPrXOXnjqe8a2nmsrd5oC7Rusk6NHv+9giQHHbHYcDipJNRuJY0jlS0lCqqDzbOJzgDA5KkngCkS4mIz5Gngep0+D/4iuiVWTekvwPJo4CnTilKim1fXm73v087emmwy38fah/bkt8LSzFxKozt8xAwA2gFQ4DYyeWBIz1wAB0GheLLm6sJ/t1srNaxqybflBCypt+UfKMbuoA4ArGjvmjOTbWDn/rxgX/2SntqF1cRvbokCpIAGWG2jQkAg4yFB6gflSVaX8zJnltGVrUIrbW97JfLtdfM6iz8VWs0jLdxRxIQ4+Vd2d5ywPPc1sQ65b3k0gtW3BmzjBB6Y7H2rktN8OSTYkufkT+73NdVaWKQRhLeIADjIFS8RO1rnQspwfNzci2tbpvf7/MsrmVI4QgCxjII6+/enq6RZCPuyMMWXAI/Oo2YICiHOfvN61HWftJXv1Ol4Sly8i+F3uuju7u+7310aLkM+1ZXBjBByBjBNQvNvjCFQoXpjP8AjUNOUgE5GeKbqza5W9CIYDDQqe1jBKXfXord+2g+MRsr73KkDK4GcmmKxU0maKzOweV3DK/iPSpoL2WHjO5fQ1WBIOQeakwJeRw/p60Aa0N7DOME7WPY0k1hby8mMA+q8VjdDg1YgvZYeM7l9DQBJLpLDJikBHo1c5qdxJaX0ELEKA/zD/P1rsIb2Gfgna3oarXeh2N7MZZoFdj3ouKxgPe2sed1xGMdiwqH+1LQsAsyMT6ZNdAPDulKMLZRKfULzUb6IsefIK/QjFMDMEgIz/IVLBeT2r74SQT1BHWnS2s0J/eRsPcdKiyDQBel1y7ngaN4k+YYJBxTtN1OO2txDKh4JwV6YJrNOO1JjilZAb6apanYm/CnrkYqQSwySfJIrA8j5u9c3+FIR6ZFJxA66VwbfhiflIPtWK8KyTEZOSecdDWas0qA7JXH0NJ5kuc72z9aOUC5PEYoz1KHjp05pbZI3DMF6VQZ5WHzOxHoTTA7D7rEfQ0coG2ifu85O1iTkmuZ8cJnQhtXP79Mhfxq3vfGNxx6ZprDIw3I9DRyjuc94VtILqw1e2njPlSRKrgtjI3etbGsaJZ69ax215GQsJ+QocFeMDHtU64XOz5c8HFODMOjmhp2silJXuzL0fwZZ6XBqwtZ5M3Nm0Pz87QSOa3gtno2hRW0K+XFFGI1THPf8ySSfxqGO5liEgGCHXaaq3afbnDSsRjsvFK0rBeLl5HQ+Dbp7mK8eQnJcYGc44rpJVUxtnqa57wfAkMN0qnqwPP410UgLDb0HrTSstRSab0M3zJS6jnPQVYLyggMRk9KZkJKykdKSWYE4A6dKTJPAwinogJ9+aeFI7gD8qTJPegGtSRxUeuaUAelJmloAdmlxTc0ooAdSim5paAHA0UgBJwBk+1SeWF++2PYdaAG1Mlu5Xc2EX1NJHcCLOyNc9ieTTHkaQ5ZiaWoD9yJwoyf7x/wppJY8nNPgtpbhgsakmuk03w6FxJc8n0o2AxrHSri9cbVIXuTXW6fo1vZKDt3Sepq9FGkShUUAD0qWKNpnCoMn+VJsqwsaNIwVBk1YmuCsQgjI2jqQOtNklSOLyIen8b929vpVeo33AWikzRVALSqCxAAyfSm8VPaukdyjyfcB5xQBFRQT8xx68UmaAFopKKAJwyyrh/lfs3r9aiZSjYYYNJmpklV1Ec3I7N3X/61AEOasw3ssOBncvoahkiaM4OCOzDoajoA24b2KbgnDehqfGehzXPVYhvZYsAncvoaBmwfQiq0tlby9YwD6jinQ30U3GcH0NT4B6GgDHm0hgSYpAfY1Rltp4T88ZA9e1dKQRTSMii4jlt1Iea6GWwt5uTGAfVeKpS6QRzE4Ps1MDKGKTn1qaW0nhPzxHHqOahOPSgBOfSkIp+4UmfQ4oAjpDmnsp9abyKAG4NNIPpUhJFNJz2oAjJ/Cjn1pxCnsaaRjvQB0vhhf3Fwc/xCtl5MEjPFYfhg7oLhQRncOK2DGWchlx+NS2AyRgzA55piFWzuH40/yljbnkGkaNSCVqNRHg+1R95/++eadvQfdT8WOahpa3EPLFjk459KXNMpRQA+jNOjhkcZAwvqeBUmII+pMre3ApXAYkbyfdUnFShIo+Xfcf7q/wCNNe5lkQITtQfwrwKjHJo16gTeecYQBF9BTKmgsbm4OI4WPvita28M3EmDM4QelGiAxURnOFBJrZ0/QZbghpPlWtu00OC3AwxJ9a0FtlAxub86LjsR2lhBZoAgGfWrfmIOrAfjUYgT0J/GtS20pIgryKpmbkIf4B6t6VEpJbjsV0iVkyz7WPRf6n2pst5DGnkwEkfxPj73/wBarF5PCuYbf5h/HIerH/CqX4ClG71YxnnDsrH8KUSsekbU/IpaoRG0kgXIjz+NU5Z9RP8AqoFHuTWgKKAMc/21J0MSfQVJDZ6kWzNd8egFavegE+tADYkKLhmLH1NSZpuc0vWgAzS0naigBaMmkpAaAJo5tq7HG6M9R6fSn3FuIwJI2EkTdGHb2PpVfIqSKdomOMFT1U9DQBGaKnkhR082A5X+JT1Wq/NAC81YhvZYuCdy+hqtk0meaANuC+jl4Bwf7pqx8rdODXOA1ZhvpIsAncvvQM2SpFNqCC+jl4VsH+6as7lbrwaAGGq81lbzfejGfUcVaKntzTKAMibRyMmKTPs1UJrWeE/PEceo5FdMaaeaLiOU3DpikOPWukmsreb70Yz6jiqEuj45if8ABqYGTnHfNISDVmayli+8jY9RzVU/L3oAD7Gkw3ajANL070AIrSRtuQlW9QcVbj1jUIekxYejDNVfxpCT2NAGtH4kkPE8Cn3U4q3Drlmz5LPHnqCOK5ps1HyKTSA8/pRk131r4IgxmZiP9nqx/Crh0rT7A7ILaPdjlm+Zv/rUe0WyFY89jtJXwSNq+rVbitJAf3Ns8rf3nHH5V232eHOfLXPuKkVQvQAfSne4WONXRdSuTmRcD3NXIfCsrf6yYD6Cuo+gpRRcdjEh8MWif6x2Y1owaTZwfcgXPqauAc0oNK4CKiLwqgD2FP6UgozxQMd9KM4NJmrNlZy3suxBhRyznoopNpK7AS1hlnmAi4xyW/uirNzeHa0MTkhj87nqx/wou7mKCM2tn/q/437uf8Kz93apS5tWgHClpKWrAKB9aKKBDhxRmm0fWgB2aOaQUAnFAC9DS5NJmkBoAXNLmk96KAF4o4NJ1o7UAL3pKQ0vSgBySNGwZDg1YKJdLuiwso6p6/SqvvSglWDA4PYigAIIOGHI6imk4q5ujvBh8LP2bs31qrJG0blXXBoAbRSZozQAdDxVmG+liwG+dfQ1VJzSZ9aBm5DeRy/dbB/umrG9T1Fc3nnIqzDfSRcN86+/WgDbK+hphBHWoIbuOX7rYP8AdNWBIDwaAG001IVB6UxlIoAaarT2NvN9+MZ9RxVmmmgDIm0YjJhk/BqoS2k8P34zj1HNdKaaaLiOUJxSFsV0k1nBN9+MZ9Rwaz5tG6mGT8GouBlbx0oyp61NLZzw53xnHqOarkA9aYGpLfyyjauI0/urVfOaQHNH0qUktgFFL1pB1pevSmAoNOzTcU4GgBRS5pvfil/GgB2aU03NT2lrJdSYHCD7zHtQ9Bj7O0e7l2j5UHLMegFWry7SKL7HZtiIfeYdXNMubxUi+y23EY6sOrVQxWduZ3YC45paTPFHpitAHZOfagHmkzxR14oAdn0oBz1pMYHFKOlAC0UZGM0CgQetKKTqKKALN3aG2SJi4JcZOB0qv0xWpqaE2FtKc8/4VlZOKAHCjPGc03NB9KAHdqSkzilzxQAZoyRRmg80AGaTPFHajrQAZxV2K4iuIxDcj5hwsncfWqOKU0AS3Fu9u+HHHYjvUPfrVy2vFCfZ7ld8JPXutJfWJtsSROJYG5Djt9aAKdFHvSE89aBhRSZo+lABnHSrMF/LHw/zr79aq0UAbUN3HL9xsH+6asCT14rnMkHIq1DfyR8N86+/WgDaIVuaYUI96rw3UcozG2D6GpxL6ikA002pjtYUxo/TmgCM0004jFNNMBDVaayt5vvxjPqODVk000gOe5p4ptOFMQuKUUgB+tGc0AO7ZoFJmlyAMUAOzxRjJ600HnmrljZSXknHyxj7zHtSbSV2MLKyku2JHyxL95z2qzd3iRxC0tSBGPvMOrGnX9+iwizteIl4Zh3rKz3qIpy1kBIeKTI70gpe9aAL2pe1IKMdKAFB+WjvSE9qXv1oELn1pc03tR2oGKT0pwPNNFHSgQ72pTTe1GcUDNzUJBLottxgrgD8qxOgrXlG/Q1PcAY/Osgn86BC0mKM8UGgYvSk9KN1HvQICaBknPajPb1oGBxQMXvSZoI5ppOKAHA5pO/BpB0zS54oAKs2l2YGw43xH7yH0qsDxSHpQBo3NkjqZ7HMkWMle61ncVPaXclpKJIzz3HY1oXNvb6khuLLCSYy8R7n2oEYxz2oJpWBXIYEEdRSH2NAwzzRSE880maAF6Cm0p9qaaAFyVPBwfarUOoSJgON6+veqfWkNAjbiuY5v9W3Pp3qcSn+IVzuSCCDg1ahv5I+JPnX9aBm3uVh2NMZM9D+dVop45RlG59O9TCQjg80gEII6immpg4YcGmlAenFAHNZJoGaTHNL9aYh3OKM0Cg5AxQAo470d+aTjr3rQ06wN5JukOyEfeb1+lKUlFXYDtM0uS/kyflhX7zn+QqxqOoJGDaWYCxLwWXvRqWqjyvsVn+7gXgkd6xyMisoxlN80/khjs0Y5/lQtL9DWwhRS5pv3frRn2oGPz+dLux1pq+/Wj+dADjR2zTScd6cDnp0oAXtQKSkoAeeO9HINJ2oxngGgBT25pQOORTc44pwNAG6ZVfw5GgUBgDk+vNYRJrctUV9EPdiGrBGc0xCgfNTvxppbHFGaQx3BNFJ0o7UgDJ/Kg4/GgUCmAZ60mOBQOuDSA4PsaAF9qCPSkNGSOtAC9sUZA6jim7uaGNAATg8U+KaSCQPG2GFMzxTQaANRjFqQyFCT4/Os6SJ4ZCkilWHrSByrBlOCOQRWxBcwanCLe6wswHyyetAjFz603+VWbq1e1lKSD6H1qv36UAJSdqU5pO1ACe+aafanH0ppoAM0ZpOlBwaAF3FTkEirUOoOvEnzD171TNJ1oA24p45hlG59O9SiRh15rntxByCQRVqDUnTAlG4evegZTHHenE8e1NUDHTmlOR25oEPBJ/Ck68U3PNaem6f9oYSzZW3HU929hUyairsBlhYm6lG7IjzyfWrOoaivl/ZLdAiLwSO9R3t8OYbYbE6ZHpWbnmla7uxjhTiRimrmlHPFWIXGO9Axik5NAPUUAOwTzTs9sUgOKARQACl6nFM5zTs7Rz1oAXBzTu5poPQmloGOB44FKemKaeDS/jxSAMcgClPBpAMUdeTTAUnJBoz196bnJxSkigDQt9TltbRrZEU5zhj2zVAelLnIzTDnqKBD+1A4NJu4oBzQMU9M0u75cGmlhmjpQACjJxSDr+NLnHFABnigUZ5+tHQ+1ACZwc0HnvQcDijtmgQlHekOc0dRQAZ7GkOAeKdTT69qAEPXilyQcjNJ9KAeKANSG/S4iEF2ASOA5/rVa8s2tzuX5oz0NVO9W7W/MZEc3zR9PpQBUzk0mQR0q/eWG1fOt/njPOB2rPPTigBD0pBRnP1o7EGgANNxxml4ApOq9aAA4PNIc0H0FITjpQAhJNNxS5GSaCc0AJkmjJHWkH61taRo32ofarkFbdecd3/APrVFSpGmrsaQmkaOLs/aLg7YF5x03f/AFqfqmoIT9mtcLGvBK/yFLq+po5+z2vyoowSOn0rGGMdazhGUnzy+7sHkOHI60H0pDQGJzitxCg84p3fFJ04xzSDrzQA9jwB3oxSUo5bFAC9+OlHcUh6kkcUA5NADu5IpTjNHYcUccetAAo9fWnMaToKQnOPagCRcYOaaeTSHIo9T3oAcDzShvypMcdaQDrQMXgHNKBwBTeoyaXnOPSgQK3PNHqKQdM0poAO1A/pR2yKO5+lAAPeg5/CkK85zQSSMUAL2NA6/Sm57Uo6+9MBQ1KeRTcAH6mlzxSGKSMcdaM/rTT1oHFAhcZ70hGG4pO3WlJ75oGIetFH3ulJnBxQIKQUY5oY5HFAAcgetGc0maXg80AWbS+a2+U/NGTyv+FWbqyS5iNxakepWso/exU9vcyWr7lOVPVexoArlSDgjBHrRWzJFb6lD5kZCyjr/wDXrJkjeGQo4wRQAw4PFNJGRRySeKTmgBMnmlDZHIpCD170nrjigAOKTFGfajJoGbWjaGbnbcXIxAOQp4Lf/WqxrOs/KbO0IVRwzL/IU3WNbBH2a0bAxhmH8hXPdTn1rjhSlUl7Sr8kO9tEP7460AgUHA6Uznr2rrJJByKUg9BTR65wKUNg/WmA7kLzSZyCewpThsDtSADOO1ACluMU9TxUZIDe9OyAOaAFBJ4zSKCCaQHjA607JxzQA7dilHAzTMc0o5oAXdu4NO4zg009Bik5JoAcTkA04YA45ph7UA96AH5yDQpOaTPenE8UAC9Tz3o388DtigAYJPWjPy0AL0HT6Uh60uc9O1G3gmgY1R1PY0445pvIOccUHls0CHZwR3pp4bkUdRSmgBG6cUA9DSHqKdjjI7GgAOCRSMe+KM/NjHFBz1oAAc/hRnnil3Ac0wnHIoAVutKMEHFMHPNKAR160AKOMjvSZ4PrSjHU8UjYNAArDNJkDtQSDz6U3ccUAL3pCTSqc4zQSM80AJng+ppKMUDPpxQA6OV4nDRnBFaqS22owbJBtmHescnvRuKsGUkMDxigCW4ge3fa3I7MO9Q5zWlDcRXUflTAZqrcWMtupkX54v7w7fWgCsRnimHIpSec005HNAwDdzQfWkPT2ozgCgQ3IYn0pQQKQkKMDrSUAPPUU7ryelN6H2HSnZz+FAxOtPB7elNHyqTSjpmgBQOc04sKQrk4zSAc+2eKBCjluetO44FIeCfWk3AGmAp6DFOPTI60nBAFGR/hSAU5UZzmhSeOKUevWkzyaAHbuwxnFGcZ70wjmnA+1AAcnHFK3LAe1KvSkB+YA9KYDh156UvB596bkc8c0p+6KQBkjNOB+U+gpo54oHTJ7UALnByKdu4pgbOPSgkgCgY7tj2pAOlC4xz0oI5PPFACn7vvQMk49qByMd6QHDAUCFPGaUHjFMPAyaOo+lADsAgUmCKAeopDkknPSgBB70oA/OlLDb75pM47dKLAIeuO1KpyPcUhJPI7Uo6/UUAIeT7Umc9fSl24B796QjNACHHT8qTnafUU7pg008nI70wFz2IpPr0NDcjrRjjmkAvFJwDSd+KQ9hQANikx707gj+dNJIFIY1m9ODWnYaiY2EUvIPy5PQ/Ws1jkE496M5AOMGmI0L3T9oaW3GU7p6fSs3Py1dtNQaH93JyhP3u61qXuix3MPn2bgvjO3s1AHNngjnrRjvmldWVirDBU4I9KQ470AMGCTmnFsdBUYBxkHinkigB/GMnrSAc0DGM0oJyMUDFzk7SeBTshegyabjb160vYY60CHLk+3qaUZHA/Om7sDFByTgUDFJ556Uu0FgT6U1sHHr6U4cAk0CHZoOD/AEqMdafz69qAHAkLk0ik56UcYwaTO38RQArHIIzzSj7o9aMD8aAe54oAeT8oxijkHAFNBHp9KXdkg0ANUnP1p+cYzQADRIc4PvQAo5OaRj6Uwk447UHHAJ5oAkT5ufag8EH1NNXp7mnAZ5PSgAY4x6UhbB9qVgO1JtKrjqaEA5Twe5pCw3dOTTQwHSlLDI45oAU9R6UhJ5p3GM9qTqfagBozn8KXOccU3nB9qAQG9qAHHuSKCSfx5pfYUikj34oAQHk80p6e9IAd+M/hSNyTntQAuaXsDUanrmlLcexoAcRkH0qNuOBxTicDmm5yMUAIG/vU7kjPcU1u1KOtFgDdg9qD+YzxUZO0ninbhjPT296AFznPpSHPX0pOuccUpGMZNAA3IOBxTMHGaduxkDnikGRnNAB14xwf0q3Y6lJaHaCSh7elUs4pMAg0AdJNp8WqQfaImHmMMgj19DXOzwyW8hjlQqffvVixv5rCYMhyp+8h6GuiSO013T8cLIuee4oA/9k=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpH4qUnIwzRv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2vsK3_rf6dg"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5O_vpKff6gE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}