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": [
+ "
"
+ ]
+ },
+ {
+ "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",
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+ "d3b3dbf67a2d4f4898d3048ef56e3383",
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+ "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='
"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset[\"train\"][\"image\"][0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z4tSgSpN_nbr"
+ },
+ "source": [
+ "Now let's take a look at the label: it is 'a conference room with a table and chairs.' The model provided an accurate label!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 56
+ },
+ "id": "TvdCuPkP4LnN",
+ "outputId": "d3c67f17-5358-468a-9fc7-99abb31a3145"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'a conference room with chairs and a table'"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset[\"train\"][\"label\"][0]"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "include_colab_link": true,
+ "machine_shape": "hm",
+ "provenance": []
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+ "gpuClass": "standard",
+ "kernelspec": {
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