diff --git a/chapters/en/Unit 3 - Vision Transformers/oneformer.mdx b/chapters/en/Unit 3 - Vision Transformers/oneformer.mdx index 170742b9c..a22b29c1d 100644 --- a/chapters/en/Unit 3 - Vision Transformers/oneformer.mdx +++ b/chapters/en/Unit 3 - Vision Transformers/oneformer.mdx @@ -50,6 +50,7 @@ from PIL import Image import requests import matplotlib.pyplot as plt + def run_segmentation(image, task_type): """Performs image segmentation based on the given task type. @@ -64,26 +65,38 @@ def run_segmentation(image, task_type): ValueError: If the task type is invalid. """ - processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_dinat_large") # Load once here - model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_dinat_large") + processor = OneFormerProcessor.from_pretrained( + "shi-labs/oneformer_ade20k_dinat_large" + ) # Load once here + model = OneFormerForUniversalSegmentation.from_pretrained( + "shi-labs/oneformer_ade20k_dinat_large" + ) if task_type == "semantic": inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt") outputs = model(**inputs) - predicted_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] + predicted_map = processor.post_process_semantic_segmentation( + outputs, target_sizes=[image.size[::-1]] + )[0] elif task_type == "instance": inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt") outputs = model(**inputs) - predicted_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] + predicted_map = processor.post_process_instance_segmentation( + outputs, target_sizes=[image.size[::-1]] + )[0]["segmentation"] elif task_type == "panoptic": inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt") outputs = model(**inputs) - predicted_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] + predicted_map = processor.post_process_panoptic_segmentation( + outputs, target_sizes=[image.size[::-1]] + )[0]["segmentation"] else: - raise ValueError("Invalid task type. Choose from 'semantic', 'instance', or 'panoptic'") + raise ValueError( + "Invalid task type. Choose from 'semantic', 'instance', or 'panoptic'" + ) return predicted_map @@ -114,11 +127,9 @@ response = requests.get(url, stream=True) response.raise_for_status() # Check for HTTP errors image = Image.open(response.raw) -task_to_run = "semantic" -predicted_map = run_segmentation(image, task_to_run) +task_to_run = "semantic" +predicted_map = run_segmentation(image, task_to_run) show_image_comparison(image, predicted_map, task_to_run) - - ``` ![semantic segmentation](https://huggingface.co/datasets/hf-vision/course-assets/resolve/main/oneformer/oneformer_semantic.png) diff --git a/chapters/en/_toctree.yml b/chapters/en/_toctree.yml index 7515e5558..8b6fa5c65 100644 --- a/chapters/en/_toctree.yml +++ b/chapters/en/_toctree.yml @@ -70,6 +70,6 @@ - local: Unit 12 - Ethics and Biases/ethics-bias-ai title: Ethics and Bias in AI - local: Unit 12 - Ethics and Biases/conclusion - title: Hugging Face's efforts: Ethics and Society + title: Hugging Face's efforts - Ethics and Society - local: Unit 12 - Ethics and Biases/supplementary-material title: Supplementary reading and resources diff --git a/notebooks/Unit 10 - Synthetic Data Creation/image_labeling_BLIP_2.ipynb b/notebooks/Unit 10 - Synthetic Data Creation/image_labeling_BLIP_2.ipynb new file mode 100644 index 000000000..8c0e7b31a --- /dev/null +++ b/notebooks/Unit 10 - Synthetic Data Creation/image_labeling_BLIP_2.ipynb @@ -0,0 +1,12070 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lbdwxvFIekQ1" + }, + "source": [ + "# Labeling a dataset using BLIP-2\n", + "\n", + "In this notebook, we will demonstrate how to create a labeled dataset using BLIP-2 and push it to the Hugging Face hub. BLIP-2, which does zero-shot image-to-text generation, was introduced in [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/pdf/2301.12597.pdf) by Li, et. al. BLIP-2 bootstraps frozen pre-trained image and LLMs, bridging the modality gap with a Querying Transformer (Q-Former). You can read more about BLIP-2 [here](https://huggingface.co/docs/transformers/v4.28.1/en/model_doc/blip-2).\n", + "\n", + "Notes:\n", + "\n", + "* It's advised to run this notebook on a GPU environment, high RAM.\n", + "\n", + "* This notebook was adapted from [here](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/BLIP-2/Chat_with_BLIP_2_%5Bint8_bitsandbytes%5D.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xzWfOdMUegig" + }, + "source": [ + "Run this cell if you have generated a large dataset. It is unnecessary for this toy example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZkGvCKBSeZv0" + }, + "outputs": [], + "source": [ + "!apt-get install git-lfs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1LsKrTuWLhJM" + }, + "source": [ + "First, let's install the dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MgaqAI5ZeSHx", + "outputId": "ab329970-94d2-4211-d951-b3fcbd2b951e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m279.7/279.7 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.6/536.6 kB\u001b[0m \u001b[31m49.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.3/38.3 MB\u001b[0m \u001b[31m38.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m19.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -q accelerate bitsandbytes datasets huggingface_hub" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8d4WLerBBuQ_" + }, + "source": [ + "Let's log into the Hugging Face hub with our \"WRITE\" access token." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 177, + "referenced_widgets": [ + "9ee866c462d44af39ab612f7ac72af96", + "7dd1afbd4fe64e1f85453f49387a6bf7", + "866a7af4ba204959a584a187f6e4c469", + "d3b3dbf67a2d4f4898d3048ef56e3383", + "e669b34c5c93456480057f969697a11b", + "befd511a0ec342d5afb48601fc09b864", + "5fdc70f275c04f9c82822edc495c46f7", + "c78491f5c4354f5a8460855416fabdab", + "38874f88530b4eefa4634ccf1e48fb1d", + "9d873176d7b94b3b8e65bc3c79f82bb1", + "ce272942821e47b09819c1905f27d6fa", + "59546604384345ed9171f955787631c6", + "afe52ddcf3aa4ba5be97c8e8f8037f55", + "09de54500e7e4f3eb1331c9bc2a6000d", + "04ae0973d4ce48b6957498a532c1b81a", + "2365b71d65bf4d979fc4f67ecaf9d45f", + "130ec764e59845cf96f987d6a31727d1", + "c81ffefc2912438ea586293aa33ddecf", + "89fe4838108d431ba30ea3f8ae7a1b08", + "ea01e4f9644c4a4dac13d92c929d6e75", + "fc7cec0b4a8d4a85a5bd318b9698e164", + "0cb0e0f7004c4f1cb20b1f85a2fe9b2b", + "2224fee0188240689d6b5d814d7dacc2", + "31db28a7cb7b4646b2b28ee252f60486", + "1a0fadad34634e588ae4d2166696708f", + "e7df87a342214d31804a31ca8f1bf215", + "7acfc9c7813d4c52a3f9c50a9423c8ed", + "0a538d76dadc42b99caeaf29140ea0c2", + "6ad379bde3d0421ca96bc0648542036e", + "34abc680c43c44948d6ca6ac2ffc5967", + "4e62e13337ee4523abe7ff3a556f4a13", + "9810977a11fc4abdb025eb13564632c4" + ] + }, + "id": "Fg9UgrYe8wU6", + "outputId": "fe3fcfe4-917d-474a-b2bf-2d02929efe5b" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9ee866c462d44af39ab612f7ac72af96", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
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