From 5cbbde89191c7aa425677028c0c8359c97aa6a0c Mon Sep 17 00:00:00 2001 From: nanguage Date: Thu, 22 Feb 2024 15:37:41 +0100 Subject: [PATCH] add ufish_finetune notebook --- notebooks/ufish_finetune.ipynb | 1520 ++++++++++++++++++++++++++++++++ 1 file changed, 1520 insertions(+) create mode 100644 notebooks/ufish_finetune.ipynb diff --git a/notebooks/ufish_finetune.ipynb b/notebooks/ufish_finetune.ipynb new file mode 100644 index 0000000..b9ed4a7 --- /dev/null +++ b/notebooks/ufish_finetune.ipynb @@ -0,0 +1,1520 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Example of U-FISH finetune\n", + "This tutorial demonstrates how to perform finetune training on U-FISH.\n" + ], + "metadata": { + "id": "TY2Fd8asoXl1" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Install dependency and download dataset" + ], + "metadata": { + "id": "wyLyg7tgsLJa" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nLwz8R3kiZPT" + }, + "outputs": [], + "source": [ + "!pip install ufish\n", + "!wget https://huggingface.co/datasets/NaNg/TestData/resolve/main/FISH_spots/suntag.zip?download=true -O suntag.zip\n", + "!unzip suntag.zip -d dataset/\n", + "!rm suntag.zip\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## File and directory structure of the dataset\n", + "\n", + "You need to prepare Test and Valid data, which are placed in different folders.\n", + "If you need to evaluate the quality of the results, you also need to prepare test data.\n", + "\n", + "These folders contain a series of image files and csv files, as shown below:\n" + ], + "metadata": { + "id": "RjSk2hp1pchb" + } + }, + { + "cell_type": "code", + "source": [ + "!ls dataset\n", + "!echo ----- Train data example -----\n", + "!ls dataset/suntag/train/ | head -n 10\n", + "!echo ...\n", + "!ls dataset/suntag/train/ | wc -l\n", + "!echo ----- Valid data example -----\n", + "!ls dataset/suntag/valid/ | head -n 10\n", + "!echo ...\n", + "!ls dataset/suntag/valid/ | wc -l\n", + "!echo ----- Test data example -----\n", + "!ls dataset/suntag/test/ | head -n 10\n", + "!echo ...\n", + "!ls dataset/suntag/test/ | wc -l" + ], + "metadata": { + "id": "4XtLvJzultnb", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e768080a-3089-48fe-a66b-45431acb28a8" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "suntag\n", + "----- Train data example -----\n", + "suntag_100.csv\n", + "suntag_100.tif\n", + "suntag_101.csv\n", + "suntag_101.tif\n", + "suntag_102.csv\n", + "suntag_102.tif\n", + "suntag_103.csv\n", + "suntag_103.tif\n", + "suntag_104.csv\n", + "suntag_104.tif\n", + "...\n", + "670\n", + "----- Valid data example -----\n", + "suntag_336.csv\n", + "suntag_336.tif\n", + "suntag_337.csv\n", + "suntag_337.tif\n", + "suntag_338.csv\n", + "suntag_338.tif\n", + "suntag_339.csv\n", + "suntag_339.tif\n", + "suntag_340.csv\n", + "suntag_340.tif\n", + "...\n", + "168\n", + "----- Test data example -----\n", + "suntag_420.csv\n", + "suntag_420.tif\n", + "suntag_421.csv\n", + "suntag_421.tif\n", + "suntag_422.csv\n", + "suntag_422.tif\n", + "suntag_423.csv\n", + "suntag_423.tif\n", + "suntag_424.csv\n", + "suntag_424.tif\n", + "...\n", + "210\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The image files need to be single-channel images and ensure that all images under the train/ directory have the same size. The recommended size is 512 * 512 pixels." + ], + "metadata": { + "id": "pwLx1XK7qIFu" + } + }, + { + "cell_type": "code", + "source": [ + "from skimage.io import imread\n", + "import matplotlib.pyplot as plt\n", + "\n", + "im0 = imread(\"./dataset/suntag/train/suntag_100.tif\")\n", + "print(\"Image shape:\", im0.shape)\n", + "plt.imshow(im0)" + ], + "metadata": { + "id": "CVzuqBrCqXaZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 470 + }, + "outputId": "6cce75e7-7313-4b78-8bd9-5fa35523336f" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Image shape: (512, 512)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 15 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The CSV file contains the coordinates of annotated points for corresponding images, with two columns (axis-0, axis-1), which correspond to the y and x coordinates.\n" + ], + "metadata": { + "id": "0zgwbUQNqTsA" + } + }, + { + "cell_type": "code", + "source": [ + "!cat dataset/suntag/train/suntag_100.csv" + ], + "metadata": { + "id": "3Oiq-g3yqEV1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "76307478-914b-41cf-e68d-7dcc98b9b860" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "axis-0,axis-1\n", + "305.5,309.5\n", + "2.50044681199995,387.684911838\n", + "337.18243060500004,444.606538934\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "df0 = pd.read_csv(\"dataset/suntag/train/suntag_100.csv\")\n", + "plt.imshow(im0)\n", + "plt.scatter(y=df0.iloc[:,0],x=df0.iloc[:,1],c='r')" + ], + "metadata": { + "id": "QeBVAiL1rt5z", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "outputId": "bfcc93fe-abbb-4b48-a8d9-9111666eefb8" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Training\n", + "\n", + "It is recommended to use Nvidia GPU for training. Use the following command to check the status of the GPU:" + ], + "metadata": { + "id": "5cAPT7XYsngm" + } + }, + { + "cell_type": "code", + "source": [ + "!nvidia-smi" + ], + "metadata": { + "id": "qOjfu5DBsjrV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6b91a8e7-574e-44af-bfba-9f2e3590692b" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Thu Feb 22 14:16:37 2024 \n", + "+---------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n", + "|-----------------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 40C P8 9W / 70W | 0MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + " \n", + "+---------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=======================================================================================|\n", + "| No running processes found |\n", + "+---------------------------------------------------------------------------------------+\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "First, we download the pre-trained weights. You can find our weights in this repository: https://huggingface.co/GangCaoLab/U-FISH/tree/main" + ], + "metadata": { + "id": "5zYWH-FCtK61" + } + }, + { + "cell_type": "code", + "source": [ + "!wget https://huggingface.co/GangCaoLab/U-FISH/resolve/main/v1.0-alldata-ufish_c32.pth?download=true -O v1.0-alldata-ufish_c32.pth" + ], + "metadata": { + "id": "mbFDe06Dtcso", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f4899346-8dd5-4e61-c66a-89ff057241b4" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-02-22 14:16:44-- https://huggingface.co/GangCaoLab/U-FISH/resolve/main/v1.0-alldata-ufish_c32.pth?download=true\n", + "Resolving huggingface.co (huggingface.co)... 18.164.174.17, 18.164.174.23, 18.164.174.55, ...\n", + "Connecting to huggingface.co (huggingface.co)|18.164.174.17|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://cdn-lfs.huggingface.co/repos/5e/81/5e819a4ba41c037c0ebc05741e83949fc4063f7c055026ed548fdd8628e2b539/41052b7d8279c4fb73b17f85ca0237f7a86f754109428bd59937e7276a23a767?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27v1.0-alldata-ufish_c32.pth%3B+filename%3D%22v1.0-alldata-ufish_c32.pth%22%3B&Expires=1708870604&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwODg3MDYwNH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy81ZS84MS81ZTgxOWE0YmE0MWMwMzdjMGViYzA1NzQxZTgzOTQ5ZmM0MDYzZjdjMDU1MDI2ZWQ1NDhmZGQ4NjI4ZTJiNTM5LzQxMDUyYjdkODI3OWM0ZmI3M2IxN2Y4NWNhMDIzN2Y3YTg2Zjc1NDEwOTQyOGJkNTk5MzdlNzI3NmEyM2E3Njc%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=NjOGJz9ARbRi6LN4RcZoBCQ%7EVOkzR9ReI2-XVOSoFA2HOEy2XyCi38sA4h01%7Etx95zCTO5fEd7nLhyJARFStjCDMTEB9mEJcKn31zOrjps4FcJ1kMU1rYAIRWby6tq4VWaYhLno43ygWFJQ2w1kIwgtrHeSCUjXQ8ikfi9F7teBx3tZOWQV4hyKzAc4m%7ECIjMmg1UuIt4B73t6uXX1VWEH4qnGpiJK3mI173JWGmXmxHh4Q4OwnPFxR%7EvAk98VdmhjcUPE2RY3U6IkHCShXYMfTZsLILd60Froerq9K-R92NeO8K8M7YAPU98fNzwnC%7E1q9kxdAKU7HAiz8wh5%7E2nA__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n", + "--2024-02-22 14:16:44-- https://cdn-lfs.huggingface.co/repos/5e/81/5e819a4ba41c037c0ebc05741e83949fc4063f7c055026ed548fdd8628e2b539/41052b7d8279c4fb73b17f85ca0237f7a86f754109428bd59937e7276a23a767?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27v1.0-alldata-ufish_c32.pth%3B+filename%3D%22v1.0-alldata-ufish_c32.pth%22%3B&Expires=1708870604&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwODg3MDYwNH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy81ZS84MS81ZTgxOWE0YmE0MWMwMzdjMGViYzA1NzQxZTgzOTQ5ZmM0MDYzZjdjMDU1MDI2ZWQ1NDhmZGQ4NjI4ZTJiNTM5LzQxMDUyYjdkODI3OWM0ZmI3M2IxN2Y4NWNhMDIzN2Y3YTg2Zjc1NDEwOTQyOGJkNTk5MzdlNzI3NmEyM2E3Njc%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=NjOGJz9ARbRi6LN4RcZoBCQ%7EVOkzR9ReI2-XVOSoFA2HOEy2XyCi38sA4h01%7Etx95zCTO5fEd7nLhyJARFStjCDMTEB9mEJcKn31zOrjps4FcJ1kMU1rYAIRWby6tq4VWaYhLno43ygWFJQ2w1kIwgtrHeSCUjXQ8ikfi9F7teBx3tZOWQV4hyKzAc4m%7ECIjMmg1UuIt4B73t6uXX1VWEH4qnGpiJK3mI173JWGmXmxHh4Q4OwnPFxR%7EvAk98VdmhjcUPE2RY3U6IkHCShXYMfTZsLILd60Froerq9K-R92NeO8K8M7YAPU98fNzwnC%7E1q9kxdAKU7HAiz8wh5%7E2nA__&Key-Pair-Id=KVTP0A1DKRTAX\n", + "Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 3.163.125.111, 3.163.125.79, 3.163.125.12, ...\n", + "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|3.163.125.111|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 685931 (670K) [binary/octet-stream]\n", + "Saving to: ‘v1.0-alldata-ufish_c32.pth’\n", + "\n", + "v1.0-alldata-ufish_ 100%[===================>] 669.85K --.-KB/s in 0.1s \n", + "\n", + "2024-02-22 14:16:44 (4.83 MB/s) - ‘v1.0-alldata-ufish_c32.pth’ saved [685931/685931]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here we use the CLI of U-FISH for training:" + ], + "metadata": { + "id": "vjNfsnmRs4G8" + } + }, + { + "cell_type": "code", + "source": [ + "!ufish load-weights v1.0-alldata-ufish_c32.pth - train -n 20 dataset/suntag/train dataset/suntag/valid --model_save_dir model_save/" + ], + "metadata": { + "id": "1b6u7Zjss_2z", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "19ec9c74-f87e-4a82-a0c7-6a263836d2d4" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[32m2024-02-22 14:17:24.081\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36minit_model\u001b[0m:\u001b[36m70\u001b[0m - \u001b[1mInitializing ufish model with kwargs: {}\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:24.081\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36minit_model\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mNumber of parameters: 162959\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:24.218\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36minit_model\u001b[0m:\u001b[36m78\u001b[0m - \u001b[1mCUDA is available, using GPU.\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:24.219\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36m_load_pth_file\u001b[0m:\u001b[36m232\u001b[0m - \u001b[1mLoading weights from v1.0-alldata-ufish_c32.pth\u001b[0m\n", + "2024-02-22 14:17:24.618327: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-02-22 14:17:24.618370: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-02-22 14:17:24.619596: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-02-22 14:17:24.626301: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-02-22 14:17:25.746352: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "\u001b[32m2024-02-22 14:17:27.288\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m733\u001b[0m - \u001b[1mUsing gaussian as target process.\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.288\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m746\u001b[0m - \u001b[1mLoading training dataset from dataset/suntag/train\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.288\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36m_load_dataset\u001b[0m:\u001b[36m650\u001b[0m - \u001b[1mLoading dataset from dir: dataset/suntag/train\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.288\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36m_load_dataset\u001b[0m:\u001b[36m651\u001b[0m - \u001b[1mImage glob: *.tif, Coordinate glob: *.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.293\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m752\u001b[0m - \u001b[1mLoading validation dataset from dataset/suntag/valid\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.294\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36m_load_dataset\u001b[0m:\u001b[36m650\u001b[0m - \u001b[1mLoading dataset from dir: dataset/suntag/valid\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.294\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36m_load_dataset\u001b[0m:\u001b[36m651\u001b[0m - \u001b[1mImage glob: *.tif, Coordinate glob: *.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.295\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m758\u001b[0m - \u001b[1mTraining dataset size: 335, Validation dataset size: 84\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.295\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m762\u001b[0m - \u001b[1mNumber of epochs: 20, Batch size: 8, Learning rate: 0.001\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.295\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtrain_on_dataset\u001b[0m:\u001b[36m149\u001b[0m - \u001b[1mLoader workers: 4, TensorBoard summary dir: runs/unet\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:27.295\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtrain_on_dataset\u001b[0m:\u001b[36m153\u001b[0m - \u001b[1mModel save dir: model_save/, Save period: 5\u001b[0m\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(_create_warning_msg(\n", + "\u001b[32m2024-02-22 14:17:27.298\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtrain_on_dataset\u001b[0m:\u001b[36m165\u001b[0m - \u001b[1mTraining using device: cuda\u001b[0m\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(_create_warning_msg(\n", + "\u001b[32m2024-02-22 14:17:31.290\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m59\u001b[0m - \u001b[1mEpoch: 1/20, Batch: 1/42, Loss: 0.5355\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:36.289\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m59\u001b[0m - \u001b[1mEpoch: 1/20, Batch: 11/42, Loss: 0.1309\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:41.192\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m59\u001b[0m - \u001b[1mEpoch: 1/20, Batch: 21/42, Loss: 0.1362\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:46.174\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m59\u001b[0m - \u001b[1mEpoch: 1/20, Batch: 31/42, Loss: 0.1078\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:51.093\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m59\u001b[0m - \u001b[1mEpoch: 1/20, Batch: 41/42, Loss: 0.0723\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:53.926\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m101\u001b[0m - \u001b[1mEpoch 1/20, Train Loss: 0.1527, Val Loss: 0.1180\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:53.934\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m111\u001b[0m - \u001b[1mBest model saved with Val Loss: 0.1180\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:53.941\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m116\u001b[0m - \u001b[1mModel saved at epoch 0.\u001b[0m\n", + "\u001b[32m2024-02-22 14:17:54.904\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m59\u001b[0m - 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\u001b[1mEpoch: 20/20, Batch: 41/42, Loss: 0.0421\u001b[0m\n", + "\u001b[32m2024-02-22 14:25:43.718\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.model.train\u001b[0m:\u001b[36mtraining_loop\u001b[0m:\u001b[36m101\u001b[0m - \u001b[1mEpoch 20/20, Train Loss: 0.0512, Val Loss: 0.0973\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "If everything goes smoothly, we expect to see a rapid decrease in loss during the training process. The finally fine-tuned model will be saved:" + ], + "metadata": { + "id": "jQBkT1Nevf_b" + } + }, + { + "cell_type": "code", + "source": [ + "!ls model_save" + ], + "metadata": { + "id": "f6nVSTiYvwla", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "89e87031-9702-4239-c8d3-9e51af30546b" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "best_model.pth\tmodel_0.pth model_10.pth model_15.pth model_5.pth\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Evaluation\n", + "\n", + "After completing the training, we can take the best-performing model on the valid dataset and validate it on the test dataset to see how well it performs." + ], + "metadata": { + "id": "_g41Xspqv85d" + } + }, + { + "cell_type": "code", + "source": [ + "# run prediction\n", + "!ufish load_weights ./model_save/best_model.pth - predict_imgs ./dataset/suntag/test/ ./predict --img_glob=\"*.tif\"" + ], + "metadata": { + "id": "wsMuYRBYv6cC", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c6013723-93d6-4588-857f-128276cc0bee" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[32m2024-02-22 14:27:37.823\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.api\u001b[0m:\u001b[36minit_model\u001b[0m:\u001b[36m70\u001b[0m - 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\u001b[1m(4/105) Predicting dataset/suntag/test/suntag_443.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.355\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_443.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.356\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.356\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.427\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_443.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.430\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - 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\u001b[1mSaved predicted spots to predict/suntag_449.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.506\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_449.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.508\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(6/105) Predicting dataset/suntag/test/suntag_490.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.508\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_490.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.510\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.510\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.513\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.573\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_490.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.576\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_490.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.577\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(7/105) Predicting dataset/suntag/test/suntag_461.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.577\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_461.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.579\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.579\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.582\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.642\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_461.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.644\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_461.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.644\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(8/105) Predicting dataset/suntag/test/suntag_518.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.644\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_518.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.646\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.646\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.705\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_518.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.709\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_518.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.709\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(9/105) Predicting dataset/suntag/test/suntag_475.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.709\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_475.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.710\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.711\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.771\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_475.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.775\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_475.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.775\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(10/105) Predicting dataset/suntag/test/suntag_451.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.775\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_451.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.777\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.777\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.781\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.834\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_451.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.836\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_451.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.837\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(11/105) Predicting dataset/suntag/test/suntag_496.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.837\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_496.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.838\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.839\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.893\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_496.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.895\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_496.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.896\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(12/105) Predicting dataset/suntag/test/suntag_450.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.896\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_450.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.897\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.898\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.949\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_450.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.951\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_450.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.951\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(13/105) Predicting dataset/suntag/test/suntag_480.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.952\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_480.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.953\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:41.953\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.004\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_480.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.007\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_480.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.007\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(14/105) Predicting dataset/suntag/test/suntag_515.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.007\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_515.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.008\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.009\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.061\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_515.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.064\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_515.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.064\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(15/105) Predicting dataset/suntag/test/suntag_508.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.065\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_508.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.066\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.066\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.118\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_508.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.121\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_508.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.121\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(16/105) Predicting dataset/suntag/test/suntag_436.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.121\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_436.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.123\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.123\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.175\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_436.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.178\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_436.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.178\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(17/105) Predicting dataset/suntag/test/suntag_420.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.179\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_420.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.180\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.180\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.223\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_420.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.225\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_420.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.225\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(18/105) Predicting dataset/suntag/test/suntag_430.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.225\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_430.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.226\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.226\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.230\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.270\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_430.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.271\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_430.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.272\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(19/105) Predicting dataset/suntag/test/suntag_510.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.272\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_510.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.273\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.273\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_510.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.315\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_510.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.315\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(20/105) Predicting dataset/suntag/test/suntag_454.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.315\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_454.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.316\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.316\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.358\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_454.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.359\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_454.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.360\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(21/105) Predicting dataset/suntag/test/suntag_435.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.360\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_435.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.361\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.361\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.404\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_435.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.405\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_435.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.406\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(22/105) Predicting dataset/suntag/test/suntag_505.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.406\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_505.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.407\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.407\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.450\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_505.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.452\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_505.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.452\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(23/105) Predicting dataset/suntag/test/suntag_457.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.452\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_457.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.453\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.453\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.457\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.495\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_457.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.497\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_457.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.497\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(24/105) Predicting dataset/suntag/test/suntag_504.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.497\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_504.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.498\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.498\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.501\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.540\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_504.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.542\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_504.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.542\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(25/105) Predicting dataset/suntag/test/suntag_437.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.542\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_437.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.543\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.544\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.587\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_437.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.588\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_437.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.589\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(26/105) Predicting dataset/suntag/test/suntag_491.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.589\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_491.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.590\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.590\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.634\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_491.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.637\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_491.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.638\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(27/105) Predicting dataset/suntag/test/suntag_460.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.638\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_460.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.640\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.640\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.683\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_460.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.685\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_460.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.685\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(28/105) Predicting dataset/suntag/test/suntag_428.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.685\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_428.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.686\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.686\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.689\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.729\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_428.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.731\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_428.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.731\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(29/105) Predicting dataset/suntag/test/suntag_500.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.731\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_500.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.732\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.732\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.735\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.786\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_500.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.789\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_500.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.789\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(30/105) Predicting dataset/suntag/test/suntag_426.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.790\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_426.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.791\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.791\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.797\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.837\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_426.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.839\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_426.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.840\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(31/105) Predicting dataset/suntag/test/suntag_513.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.840\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_513.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.841\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.841\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.885\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_513.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.887\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_513.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.887\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(32/105) Predicting dataset/suntag/test/suntag_498.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.887\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_498.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.888\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.889\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.931\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_498.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.933\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_498.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.933\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(33/105) Predicting dataset/suntag/test/suntag_421.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.934\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_421.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.935\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.935\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.981\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_421.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.983\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_421.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.983\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(34/105) Predicting dataset/suntag/test/suntag_433.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.983\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_433.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.985\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:42.985\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.027\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_433.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.029\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_433.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.029\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(35/105) Predicting dataset/suntag/test/suntag_486.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.029\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_486.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.030\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.030\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.073\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_486.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.075\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_486.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.075\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(36/105) Predicting dataset/suntag/test/suntag_488.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.075\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_488.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.076\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.076\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.118\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_488.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.120\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_488.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.120\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(37/105) Predicting dataset/suntag/test/suntag_463.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.120\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_463.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.121\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.121\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.124\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.165\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_463.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.167\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_463.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.167\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(38/105) Predicting dataset/suntag/test/suntag_479.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.167\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_479.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.168\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.168\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.209\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_479.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.211\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_479.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.212\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(39/105) Predicting dataset/suntag/test/suntag_468.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.212\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_468.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.213\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.213\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.257\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_468.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.258\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_468.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.258\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(40/105) Predicting dataset/suntag/test/suntag_467.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.259\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_467.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.260\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.260\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.301\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_467.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.302\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_467.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.303\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(41/105) Predicting dataset/suntag/test/suntag_464.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.303\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_464.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.304\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.304\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.306\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.348\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_464.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.349\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_464.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.350\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(42/105) Predicting dataset/suntag/test/suntag_489.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.350\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_489.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.351\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.351\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.393\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_489.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.394\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_489.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.395\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(43/105) Predicting dataset/suntag/test/suntag_487.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.395\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_487.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.396\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.396\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.400\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.440\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_487.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.442\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_487.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.443\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(44/105) Predicting dataset/suntag/test/suntag_482.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.443\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_482.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.444\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.444\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.486\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_482.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.488\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_482.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.489\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(45/105) Predicting dataset/suntag/test/suntag_483.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.489\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_483.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.490\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.490\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.532\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_483.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.534\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_483.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.534\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(46/105) Predicting dataset/suntag/test/suntag_422.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.534\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_422.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.535\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.535\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.578\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_422.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.579\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_422.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.580\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(47/105) Predicting dataset/suntag/test/suntag_448.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.580\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_448.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.581\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.581\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.625\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_448.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.626\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_448.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.626\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(48/105) Predicting dataset/suntag/test/suntag_465.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.626\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_465.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.627\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.627\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.669\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_465.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.671\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_465.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.672\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(49/105) Predicting dataset/suntag/test/suntag_501.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.672\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_501.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.673\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.673\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.676\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.715\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_501.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.716\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_501.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.717\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(50/105) Predicting dataset/suntag/test/suntag_446.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.717\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_446.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.718\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.718\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.722\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.760\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_446.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.762\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_446.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.762\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(51/105) Predicting dataset/suntag/test/suntag_434.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.762\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_434.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.763\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.763\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.765\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.807\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_434.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.809\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_434.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.810\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(52/105) Predicting dataset/suntag/test/suntag_466.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.810\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_466.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.811\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.812\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.855\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_466.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.856\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_466.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.856\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(53/105) Predicting dataset/suntag/test/suntag_431.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.857\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_431.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.858\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.858\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.900\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_431.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.901\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_431.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.902\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(54/105) Predicting dataset/suntag/test/suntag_523.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.902\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_523.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.903\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.903\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.905\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.943\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_523.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.945\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_523.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.945\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(55/105) Predicting dataset/suntag/test/suntag_502.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.945\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_502.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.946\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.946\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.949\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.987\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_502.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.989\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_502.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.989\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(56/105) Predicting dataset/suntag/test/suntag_469.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.989\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_469.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.990\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:43.990\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.033\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_469.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.034\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_469.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.034\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(57/105) Predicting dataset/suntag/test/suntag_438.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.034\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_438.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.035\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.036\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.038\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.076\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_438.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.078\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_438.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.078\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(58/105) Predicting dataset/suntag/test/suntag_516.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.078\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_516.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.079\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.079\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.119\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_516.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.121\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_516.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.121\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(59/105) Predicting dataset/suntag/test/suntag_494.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.121\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_494.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.122\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.122\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.162\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_494.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.163\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_494.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.163\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(60/105) Predicting dataset/suntag/test/suntag_520.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.163\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_520.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.164\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.165\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.204\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_520.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.206\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_520.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.206\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(61/105) Predicting dataset/suntag/test/suntag_507.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.206\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_507.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.207\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.207\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.250\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_507.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.252\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_507.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.252\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(62/105) Predicting dataset/suntag/test/suntag_481.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.252\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_481.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.253\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.253\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.256\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.294\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_481.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.295\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_481.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.295\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(63/105) Predicting dataset/suntag/test/suntag_509.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.296\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_509.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.296\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.296\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.298\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.336\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_509.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.337\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_509.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.337\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(64/105) Predicting dataset/suntag/test/suntag_485.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.338\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_485.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.339\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.339\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.341\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.379\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_485.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.380\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_485.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.380\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(65/105) Predicting dataset/suntag/test/suntag_423.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.381\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_423.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.381\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.382\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.424\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_423.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.426\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_423.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.427\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(66/105) Predicting dataset/suntag/test/suntag_522.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.427\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_522.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.428\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.428\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.471\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_522.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.473\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_522.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.473\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(67/105) Predicting dataset/suntag/test/suntag_484.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.473\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_484.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.474\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.474\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.516\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_484.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.518\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_484.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.518\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(68/105) Predicting dataset/suntag/test/suntag_453.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.518\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_453.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.519\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.519\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.561\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_453.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.563\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_453.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.563\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(69/105) Predicting dataset/suntag/test/suntag_441.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.563\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_441.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.564\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.564\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.604\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_441.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.606\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_441.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.606\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(70/105) Predicting dataset/suntag/test/suntag_517.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.606\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_517.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.607\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.607\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.651\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_517.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.653\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_517.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.653\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(71/105) Predicting dataset/suntag/test/suntag_440.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.654\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_440.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.655\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.655\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.658\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.697\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_440.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.699\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_440.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.699\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(72/105) Predicting dataset/suntag/test/suntag_492.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.699\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_492.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.700\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.701\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.703\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.741\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_492.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.743\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_492.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.743\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(73/105) Predicting dataset/suntag/test/suntag_476.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.743\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_476.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.745\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.745\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.747\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.785\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_476.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.787\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_476.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.787\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(74/105) Predicting dataset/suntag/test/suntag_452.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.788\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_452.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.789\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.789\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.791\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.829\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_452.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.831\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_452.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.831\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(75/105) Predicting dataset/suntag/test/suntag_432.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.831\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_432.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.832\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.832\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.835\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.875\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_432.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.877\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_432.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.878\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(76/105) Predicting dataset/suntag/test/suntag_470.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.878\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_470.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.879\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.879\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.922\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_470.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.923\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_470.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.924\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(77/105) Predicting dataset/suntag/test/suntag_442.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.924\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_442.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.925\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.925\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.927\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.967\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_442.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.968\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_442.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.969\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(78/105) Predicting dataset/suntag/test/suntag_429.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.969\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_429.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.970\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.970\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:44.973\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.012\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_429.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.013\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_429.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.014\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(79/105) Predicting dataset/suntag/test/suntag_458.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.014\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_458.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.015\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.015\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.018\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.057\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_458.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.058\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_458.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.059\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(80/105) Predicting dataset/suntag/test/suntag_477.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.059\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_477.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.060\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.060\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.063\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.103\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_477.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.104\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_477.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.105\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(81/105) Predicting dataset/suntag/test/suntag_444.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.105\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_444.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.106\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.106\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.147\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_444.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.149\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_444.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.149\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(82/105) Predicting dataset/suntag/test/suntag_519.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.149\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_519.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.150\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.150\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.194\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_519.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.196\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_519.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.196\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(83/105) Predicting dataset/suntag/test/suntag_462.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.196\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_462.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.197\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.197\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.239\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_462.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.240\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_462.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.241\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(84/105) Predicting dataset/suntag/test/suntag_445.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.241\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_445.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.242\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.242\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.244\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.283\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_445.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.285\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_445.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.285\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(85/105) Predicting dataset/suntag/test/suntag_514.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.285\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_514.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.286\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.286\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.327\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_514.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.329\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_514.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.329\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(86/105) Predicting dataset/suntag/test/suntag_447.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.329\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_447.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.330\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.330\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.333\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.372\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_447.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.373\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_447.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.373\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(87/105) Predicting dataset/suntag/test/suntag_511.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.374\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_511.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.375\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.375\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.378\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.417\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_511.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.419\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_511.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.420\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(88/105) Predicting dataset/suntag/test/suntag_497.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.420\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_497.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.421\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.421\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.463\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_497.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.464\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_497.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.464\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(89/105) Predicting dataset/suntag/test/suntag_495.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.464\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_495.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.465\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.466\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.507\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_495.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.508\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_495.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.509\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(90/105) Predicting dataset/suntag/test/suntag_456.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.509\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_456.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.510\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.510\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.513\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.555\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_456.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.557\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_456.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.558\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(91/105) Predicting dataset/suntag/test/suntag_503.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.558\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_503.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.559\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.559\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.601\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_503.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.602\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_503.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.602\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(92/105) Predicting dataset/suntag/test/suntag_506.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.602\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_506.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.603\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.603\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.606\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.646\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_506.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.648\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_506.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.648\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(93/105) Predicting dataset/suntag/test/suntag_474.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.649\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_474.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.650\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.650\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.692\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_474.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.694\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_474.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.694\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(94/105) Predicting dataset/suntag/test/suntag_427.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.694\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_427.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.695\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.695\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.697\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.735\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_427.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.737\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_427.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.737\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(95/105) Predicting dataset/suntag/test/suntag_471.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.737\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_471.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.738\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.738\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.741\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.779\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_471.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.781\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_471.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.781\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(96/105) Predicting dataset/suntag/test/suntag_478.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.782\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_478.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.783\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.783\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.785\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.825\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_478.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.826\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_478.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.826\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(97/105) Predicting dataset/suntag/test/suntag_499.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.827\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_499.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.827\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.828\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.830\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.873\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_499.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.875\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_499.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.876\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(98/105) Predicting dataset/suntag/test/suntag_512.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.876\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_512.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.877\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.877\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.882\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.922\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_512.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.923\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_512.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.924\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(99/105) Predicting dataset/suntag/test/suntag_521.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.924\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_521.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.925\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.925\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.966\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_521.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.968\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_521.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.968\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(100/105) Predicting dataset/suntag/test/suntag_455.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.968\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_455.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.969\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:45.969\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.012\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_455.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.013\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_455.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.014\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(101/105) Predicting dataset/suntag/test/suntag_472.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.014\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_472.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.015\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.015\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.059\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_472.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.061\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_472.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.062\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(102/105) Predicting dataset/suntag/test/suntag_425.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.062\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_425.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.063\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.063\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.067\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.107\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_425.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.109\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_425.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.109\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(103/105) Predicting dataset/suntag/test/suntag_424.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.109\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_424.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.110\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.111\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.114\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.154\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_424.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.156\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_424.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.157\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(104/105) Predicting dataset/suntag/test/suntag_459.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.157\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_459.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.158\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.158\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.200\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_459.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.201\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_459.enhanced.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.201\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict_imgs\u001b[0m:\u001b[36m243\u001b[0m - \u001b[1m(105/105) Predicting dataset/suntag/test/suntag_493.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.202\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m166\u001b[0m - \u001b[1mPredicting dataset/suntag/test/suntag_493.tif\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.202\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mAxes not specified, infering from image shape.\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.203\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m173\u001b[0m - \u001b[1mInfered axes: yx, image shape: (512, 512)\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.205\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36mscale_image\u001b[0m:\u001b[36m31\u001b[0m - \u001b[33m\u001b[1mImage has outlier values. \u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.244\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mpredict\u001b[0m:\u001b[36m190\u001b[0m - \u001b[1mSaved predicted spots to predict/suntag_493.pred.csv\u001b[0m\n", + "\u001b[32m2024-02-22 14:27:46.245\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.utils.img\u001b[0m:\u001b[36msave_enhimg\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mSaved enhanced image to predict/suntag_493.enhanced.tif\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# run evaluation\n", + "!ufish evaluate_imgs ./predict/ ./dataset/suntag/test ./eval.csv" + ], + "metadata": { + "id": "68mviJrG9Whl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "00f23364-eb46-41bb-8773-cd47b1f772de" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[32m2024-02-22 14:28:01.377\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m384\u001b[0m - \u001b[1mEvaluating 105 images\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.497\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (1/105) suntag_518, f1(cutoff=3): 1.0000, pred num: 3, true num: 3, true positive: 3, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.5974\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.502\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (2/105) suntag_428, f1(cutoff=3): 0.6667, pred num: 3, true num: 6, true positive: 3, false negative: 3, false positive: 0, recall: 0.5000, precision: 1.0000, mean distance: 0.6097\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.506\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (3/105) suntag_516, f1(cutoff=3): 1.0000, pred num: 10, true num: 10, true positive: 10, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.6215\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.509\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (4/105) suntag_426, f1(cutoff=3): 0.7826, pred num: 9, true num: 14, true positive: 9, false negative: 5, false positive: 0, recall: 0.6429, precision: 1.0000, mean distance: 0.4029\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.513\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (5/105) suntag_432, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.6199\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.517\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (6/105) suntag_494, f1(cutoff=3): 0.0000, pred num: 0, true num: 0, true positive: 0, false negative: 0, false positive: 0, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.521\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (7/105) suntag_440, f1(cutoff=3): 0.9333, pred num: 8, true num: 7, true positive: 7, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.8750, mean distance: 0.6008\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.525\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (8/105) suntag_504, f1(cutoff=3): 1.0000, pred num: 4, true num: 4, true positive: 4, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4800\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.529\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (9/105) suntag_421, f1(cutoff=3): 1.0000, pred num: 8, true num: 8, true positive: 8, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.5606\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.532\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (10/105) suntag_510, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4157\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.536\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (11/105) suntag_476, f1(cutoff=3): 0.9231, pred num: 13, true num: 13, true positive: 12, false negative: 1, false positive: 1, recall: 0.9231, precision: 0.9231, mean distance: 0.5376\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.540\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (12/105) suntag_472, f1(cutoff=3): 0.9091, pred num: 6, true num: 5, true positive: 5, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.8333, mean distance: 0.5205\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.543\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (13/105) suntag_478, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4018\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.547\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (14/105) suntag_479, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.2007\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.551\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (15/105) suntag_497, f1(cutoff=3): 1.0000, pred num: 3, true num: 3, true positive: 3, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.5154\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.554\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (16/105) suntag_434, f1(cutoff=3): 1.0000, pred num: 3, true num: 3, true positive: 3, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3018\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.558\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (17/105) suntag_498, f1(cutoff=3): 0.3750, pred num: 3, true num: 13, true positive: 3, false negative: 10, false positive: 0, recall: 0.2308, precision: 1.0000, mean distance: 0.5400\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.562\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (18/105) suntag_436, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.2897\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.565\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (19/105) suntag_519, f1(cutoff=3): 0.0000, pred num: 0, true num: 0, true positive: 0, false negative: 0, false positive: 0, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.567\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (20/105) suntag_482, f1(cutoff=3): 0.6667, pred num: 1, true num: 2, true positive: 1, false negative: 1, false positive: 0, recall: 0.5000, precision: 1.0000, mean distance: 1.7159\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.570\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (21/105) suntag_430, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4119\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.573\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (22/105) suntag_514, f1(cutoff=3): 1.0000, pred num: 11, true num: 11, true positive: 11, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4040\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.576\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (23/105) suntag_464, f1(cutoff=3): 1.0000, pred num: 5, true num: 5, true positive: 5, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4642\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.579\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (24/105) suntag_523, f1(cutoff=3): 1.0000, pred num: 5, true num: 5, true positive: 5, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4916\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.582\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (25/105) suntag_487, f1(cutoff=3): 0.8000, pred num: 4, true num: 6, true positive: 4, false negative: 2, false positive: 0, recall: 0.6667, precision: 1.0000, mean distance: 0.6302\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.584\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (26/105) suntag_473, f1(cutoff=3): 1.0000, pred num: 10, true num: 10, true positive: 10, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.5376\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.587\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (27/105) suntag_512, f1(cutoff=3): 1.0000, pred num: 3, true num: 3, true positive: 3, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4699\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.590\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (28/105) suntag_443, f1(cutoff=3): 0.8000, pred num: 7, true num: 8, true positive: 6, false negative: 2, false positive: 1, recall: 0.7500, precision: 0.8571, mean distance: 0.4883\u001b[0m\n", + "/usr/local/lib/python3.10/dist-packages/ufish/utils/metrics.py:28: RuntimeWarning: Mean of empty slice.\n", + " mean_dist = dist[tp_idx].mean()\n", + "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "\u001b[32m2024-02-22 14:28:01.595\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (29/105) suntag_444, f1(cutoff=3): 0.0000, pred num: 11, true num: 2, true positive: 0, false negative: 2, false positive: 11, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.599\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (30/105) suntag_508, f1(cutoff=3): 0.6667, pred num: 1, true num: 2, true positive: 1, false negative: 1, false positive: 0, recall: 0.5000, precision: 1.0000, mean distance: 2.3169\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.603\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (31/105) suntag_468, f1(cutoff=3): 0.4545, pred num: 5, true num: 17, true positive: 5, false negative: 12, false positive: 0, recall: 0.2941, precision: 1.0000, mean distance: 0.4551\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.607\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (32/105) suntag_423, f1(cutoff=3): 0.0000, pred num: 0, true num: 0, true positive: 0, false negative: 0, false positive: 0, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.611\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (33/105) suntag_503, f1(cutoff=3): 0.6667, pred num: 1, true num: 2, true positive: 1, false negative: 1, false positive: 0, recall: 0.5000, precision: 1.0000, mean distance: 0.2846\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.614\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (34/105) suntag_425, f1(cutoff=3): 0.9412, pred num: 8, true num: 9, true positive: 8, false negative: 1, false positive: 0, recall: 0.8889, precision: 1.0000, mean distance: 0.4155\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.617\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (35/105) suntag_483, f1(cutoff=3): 1.0000, pred num: 9, true num: 9, true positive: 9, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4093\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.620\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (36/105) suntag_446, f1(cutoff=3): 1.0000, pred num: 3, true num: 3, true positive: 3, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.7310\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.623\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (37/105) suntag_465, f1(cutoff=3): 0.0000, pred num: 0, true num: 0, true positive: 0, false negative: 0, false positive: 0, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "/usr/local/lib/python3.10/dist-packages/ufish/utils/metrics.py:28: RuntimeWarning: Mean of empty slice.\n", + " mean_dist = dist[tp_idx].mean()\n", + "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "\u001b[32m2024-02-22 14:28:01.626\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (38/105) suntag_470, f1(cutoff=3): 0.0000, pred num: 3, true num: 15, true positive: 0, false negative: 15, false positive: 3, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.629\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (39/105) suntag_463, f1(cutoff=3): 0.6667, pred num: 2, true num: 1, true positive: 1, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.5000, mean distance: 0.4442\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.632\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (40/105) suntag_491, f1(cutoff=3): 0.9091, pred num: 6, true num: 5, true positive: 5, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.8333, mean distance: 0.4758\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.635\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (41/105) suntag_506, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3734\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.638\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (42/105) suntag_474, f1(cutoff=3): 0.8889, pred num: 5, true num: 4, true positive: 4, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.8000, mean distance: 0.5649\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.641\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (43/105) suntag_524, f1(cutoff=3): 1.0000, pred num: 3, true num: 3, true positive: 3, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.5905\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.645\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (44/105) suntag_469, f1(cutoff=3): 0.6667, pred num: 1, true num: 2, true positive: 1, false negative: 1, false positive: 0, recall: 0.5000, precision: 1.0000, mean distance: 0.7906\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.649\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (45/105) suntag_488, f1(cutoff=3): 0.8889, pred num: 5, true num: 4, true positive: 4, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.8000, mean distance: 0.3751\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.653\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (46/105) suntag_452, f1(cutoff=3): 0.7692, pred num: 8, true num: 5, true positive: 5, false negative: 0, false positive: 3, recall: 1.0000, precision: 0.6250, mean distance: 0.4590\u001b[0m\n", + "/usr/local/lib/python3.10/dist-packages/ufish/utils/metrics.py:28: RuntimeWarning: Mean of empty slice.\n", + " mean_dist = dist[tp_idx].mean()\n", + "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "\u001b[32m2024-02-22 14:28:01.655\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (47/105) suntag_442, f1(cutoff=3): 0.0000, pred num: 1, true num: 1, true positive: 0, false negative: 1, false positive: 1, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.658\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (48/105) suntag_438, f1(cutoff=3): 0.8000, pred num: 5, true num: 5, true positive: 4, false negative: 1, false positive: 1, recall: 0.8000, precision: 0.8000, mean distance: 0.5958\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.661\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (49/105) suntag_453, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.7071\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.664\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (50/105) suntag_466, f1(cutoff=3): 0.8571, pred num: 18, true num: 17, true positive: 15, false negative: 2, false positive: 3, recall: 0.8824, precision: 0.8333, mean distance: 0.4922\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.667\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (51/105) suntag_458, f1(cutoff=3): 1.0000, pred num: 4, true num: 4, true positive: 4, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4465\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.670\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (52/105) suntag_459, f1(cutoff=3): 0.9630, pred num: 13, true num: 14, true positive: 13, false negative: 1, false positive: 0, recall: 0.9286, precision: 1.0000, mean distance: 0.4604\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.672\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (53/105) suntag_451, f1(cutoff=3): 0.8333, pred num: 5, true num: 7, true positive: 5, false negative: 2, false positive: 0, recall: 0.7143, precision: 1.0000, mean distance: 0.4359\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.676\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (54/105) suntag_501, f1(cutoff=3): 0.8333, pred num: 5, true num: 7, true positive: 5, false negative: 2, false positive: 0, recall: 0.7143, precision: 1.0000, mean distance: 0.6519\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.680\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (55/105) suntag_511, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3761\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.684\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (56/105) suntag_485, f1(cutoff=3): 1.0000, pred num: 5, true num: 5, true positive: 5, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4550\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.687\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (57/105) suntag_521, f1(cutoff=3): 0.7778, pred num: 8, true num: 10, true positive: 7, false negative: 3, false positive: 1, recall: 0.7000, precision: 0.8750, mean distance: 0.4881\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.690\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (58/105) suntag_481, f1(cutoff=3): 1.0000, pred num: 4, true num: 4, true positive: 4, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3718\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.693\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (59/105) suntag_495, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.2357\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.695\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (60/105) suntag_492, f1(cutoff=3): 0.9565, pred num: 12, true num: 11, true positive: 11, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.9167, mean distance: 0.5692\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.699\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (61/105) suntag_507, f1(cutoff=3): 0.8333, pred num: 6, true num: 6, true positive: 5, false negative: 1, false positive: 1, recall: 0.8333, precision: 0.8333, mean distance: 0.6523\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.702\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (62/105) suntag_520, f1(cutoff=3): 1.0000, pred num: 6, true num: 6, true positive: 6, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3601\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.705\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (63/105) suntag_477, f1(cutoff=3): 1.0000, pred num: 6, true num: 6, true positive: 6, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.5444\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.708\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (64/105) suntag_439, f1(cutoff=3): 0.0000, pred num: 0, true num: 0, true positive: 0, false negative: 0, false positive: 0, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "/usr/local/lib/python3.10/dist-packages/ufish/utils/metrics.py:28: RuntimeWarning: Mean of empty slice.\n", + " mean_dist = dist[tp_idx].mean()\n", + "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "\u001b[32m2024-02-22 14:28:01.711\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (65/105) suntag_433, f1(cutoff=3): 0.0000, pred num: 1, true num: 7, true positive: 0, false negative: 7, false positive: 1, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.714\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (66/105) suntag_457, f1(cutoff=3): 1.0000, pred num: 4, true num: 4, true positive: 4, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3819\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.717\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (67/105) suntag_509, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.7161\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.721\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (68/105) suntag_437, f1(cutoff=3): 0.8276, pred num: 15, true num: 14, true positive: 12, false negative: 2, false positive: 3, recall: 0.8571, precision: 0.8000, mean distance: 0.6504\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.724\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (69/105) suntag_462, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 1.0404\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.727\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (70/105) suntag_505, f1(cutoff=3): 0.9091, pred num: 5, true num: 6, true positive: 5, false negative: 1, false positive: 0, recall: 0.8333, precision: 1.0000, mean distance: 0.4866\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.730\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (71/105) suntag_467, f1(cutoff=3): 1.0000, pred num: 10, true num: 10, true positive: 10, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4343\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.732\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (72/105) suntag_449, f1(cutoff=3): 0.7273, pred num: 13, true num: 20, true positive: 12, false negative: 8, false positive: 1, recall: 0.6000, precision: 0.9231, mean distance: 0.3909\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.735\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (73/105) suntag_471, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.2768\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.738\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (74/105) suntag_500, f1(cutoff=3): 1.0000, pred num: 7, true num: 7, true positive: 7, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3763\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.741\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (75/105) suntag_486, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3072\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.744\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (76/105) suntag_513, f1(cutoff=3): 0.8750, pred num: 9, true num: 7, true positive: 7, false negative: 0, false positive: 2, recall: 1.0000, precision: 0.7778, mean distance: 0.4508\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.747\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (77/105) suntag_441, f1(cutoff=3): 0.9600, pred num: 13, true num: 12, true positive: 12, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.9231, mean distance: 0.4706\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.750\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (78/105) suntag_490, f1(cutoff=3): 0.8571, pred num: 3, true num: 4, true positive: 3, false negative: 1, false positive: 0, recall: 0.7500, precision: 1.0000, mean distance: 0.8303\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.753\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (79/105) suntag_429, f1(cutoff=3): 1.0000, pred num: 7, true num: 7, true positive: 7, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4043\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.756\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (80/105) suntag_484, f1(cutoff=3): 0.0000, pred num: 0, true num: 0, true positive: 0, false negative: 0, false positive: 0, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.759\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (81/105) suntag_517, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3536\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.761\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (82/105) suntag_435, f1(cutoff=3): 0.9000, pred num: 10, true num: 10, true positive: 9, false negative: 1, false positive: 1, recall: 0.9000, precision: 0.9000, mean distance: 0.5273\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.764\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (83/105) suntag_480, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 1.1785\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.767\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (84/105) suntag_499, f1(cutoff=3): 0.9333, pred num: 8, true num: 7, true positive: 7, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.8750, mean distance: 0.3873\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.770\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (85/105) suntag_461, f1(cutoff=3): 0.9333, pred num: 7, true num: 8, true positive: 7, false negative: 1, false positive: 0, recall: 0.8750, precision: 1.0000, mean distance: 0.6514\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.772\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (86/105) suntag_475, f1(cutoff=3): 0.4000, pred num: 3, true num: 12, true positive: 3, false negative: 9, false positive: 0, recall: 0.2500, precision: 1.0000, mean distance: 0.6683\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.775\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (87/105) suntag_447, f1(cutoff=3): 0.8889, pred num: 8, true num: 10, true positive: 8, false negative: 2, false positive: 0, recall: 0.8000, precision: 1.0000, mean distance: 0.5695\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.778\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (88/105) suntag_445, f1(cutoff=3): 0.9333, pred num: 7, true num: 8, true positive: 7, false negative: 1, false positive: 0, recall: 0.8750, precision: 1.0000, mean distance: 0.3928\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.781\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (89/105) suntag_454, f1(cutoff=3): 0.9474, pred num: 18, true num: 20, true positive: 18, false negative: 2, false positive: 0, recall: 0.9000, precision: 1.0000, mean distance: 0.3644\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.783\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (90/105) suntag_427, f1(cutoff=3): 0.8333, pred num: 5, true num: 7, true positive: 5, false negative: 2, false positive: 0, recall: 0.7143, precision: 1.0000, mean distance: 0.5373\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.786\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (91/105) suntag_424, f1(cutoff=3): 0.0000, pred num: 0, true num: 1, true positive: 0, false negative: 1, false positive: 0, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.789\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (92/105) suntag_420, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.3536\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.792\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (93/105) suntag_450, f1(cutoff=3): 0.9630, pred num: 14, true num: 13, true positive: 13, false negative: 0, false positive: 1, recall: 1.0000, precision: 0.9286, mean distance: 0.4640\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.795\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (94/105) suntag_493, f1(cutoff=3): 0.0000, pred num: 0, true num: 1, true positive: 0, false negative: 1, false positive: 0, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.797\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (95/105) suntag_460, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 1.3060\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.800\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (96/105) suntag_515, f1(cutoff=3): 0.9333, pred num: 7, true num: 8, true positive: 7, false negative: 1, false positive: 0, recall: 0.8750, precision: 1.0000, mean distance: 0.8887\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.803\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (97/105) suntag_422, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.7500\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.806\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (98/105) suntag_431, f1(cutoff=3): 1.0000, pred num: 1, true num: 1, true positive: 1, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.2366\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.808\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (99/105) suntag_456, f1(cutoff=3): 1.0000, pred num: 8, true num: 8, true positive: 8, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.4365\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.811\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (100/105) suntag_502, f1(cutoff=3): 1.0000, pred num: 5, true num: 5, true positive: 5, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.5381\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.814\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (101/105) suntag_522, f1(cutoff=3): 0.0000, pred num: 0, true num: 0, true positive: 0, false negative: 0, false positive: 0, recall: 0.0000, precision: 0.0000, mean distance: nan\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.817\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (102/105) suntag_496, f1(cutoff=3): 0.4242, pred num: 7, true num: 26, true positive: 7, false negative: 19, false positive: 0, recall: 0.2692, precision: 1.0000, mean distance: 0.6852\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.820\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (103/105) suntag_455, f1(cutoff=3): 1.0000, pred num: 2, true num: 2, true positive: 2, false negative: 0, false positive: 0, recall: 1.0000, precision: 1.0000, mean distance: 0.6591\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.823\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (104/105) suntag_489, f1(cutoff=3): 0.7500, pred num: 10, true num: 6, true positive: 6, false negative: 0, false positive: 4, recall: 1.0000, precision: 0.6000, mean distance: 0.3174\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.825\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mEvaluated (105/105) suntag_448, f1(cutoff=3): 0.5714, pred num: 2, true num: 5, true positive: 2, false negative: 3, false positive: 0, recall: 0.4000, precision: 1.0000, mean distance: 0.4025\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.827\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m451\u001b[0m - \u001b[1mMean f1(cutoff=3): 0.7885\u001b[0m\n", + "\u001b[32m2024-02-22 14:28:01.827\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mufish.cli\u001b[0m:\u001b[36mevaluate_imgs\u001b[0m:\u001b[36m452\u001b[0m - \u001b[1mSaving results to ./eval.csv\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "eval_df = pd.read_csv(\"./eval.csv\")\n", + "eval_df = eval_df[eval_df['true num'] != 0] # remove empty image\n", + "print(\"Mean f1(cutoff=3.0):\", eval_df['f1(cutoff=3)'].mean())" + ], + "metadata": { + "id": "gKSbaXL9_Ea6", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "196b5b63-ee82-4256-c1c5-20d3a4df2bee" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean f1(cutoff=3.0): 0.8448672164793175\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We can draw out and take a look at some results." + ], + "metadata": { + "id": "Dq_moQGRBupV" + } + }, + { + "cell_type": "code", + "source": [ + "from ufish.cli import UFishCLI\n", + "\n", + "ufish = UFishCLI()\n", + "sample = \"suntag_425\"\n", + "ufish.plot_2d_eval(f\"./dataset/suntag/test/{sample}.tif\", f\"./dataset/suntag/test/{sample}.csv\", f\"./predict/{sample}.pred.csv\")" + ], + "metadata": { + "id": "BYzddSIKB2DV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 853 + }, + "outputId": "6d70f1d6-47f4-4a00-9f43-4fe72fafb03f" + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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