<p align="center"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/logo_name_transparent.png" alt="drawing" width="250"/> </p>

HugsVision is an open-source and easy to use all-in-one huggingface wrapper for computer vision.

The goal is to create a fast, flexible and user-friendly toolkit that can be used to easily develop state-of-the-art computer vision technologies, including systems for Image Classification, Semantic Segmentation, Object Detection, Image Generation, Denoising and much more.

⚠️ HugsVision is currently in beta. ⚠️

Quick installation

HugsVision is constantly evolving. New features, tutorials, and documentation will appear over time. HugsVision can be installed via PyPI to rapidly use the standard library. Moreover, a local installation can be used by those users than want to run experiments and modify/customize the toolkit. HugsVision supports both CPU and GPU computations. For most recipes, however, a GPU is necessary during training. Please note that CUDA must be properly installed to use GPUs.

Anaconda setup

conda create --name HugsVision python=3.6 -y
conda activate HugsVision

More information on managing environments with Anaconda can be found in the conda cheat sheet.

Install via PyPI

Once you have created your Python environment (Python 3.6+) you can simply type:

pip install hugsvision

Install with GitHub

Once you have created your Python environment (Python 3.6+) you can simply type:

git clone https://github.com/qanastek/HugsVision.git
cd HugsVision
pip install -r requirements.txt
pip install --editable .

Any modification made to the hugsvision package will be automatically interpreted as we installed it with the --editable flag.

Example Usage

Let's train a binary classifier that can distinguish people with or without Pneumothorax thanks to their radiography.

Steps:

1. Move to the recipe directory cd recipes/pneumothorax/binary_classification/
2. Download the dataset here ~779 MB.
3. Transform the dataset into a directory based one, thanks to the process.py script.
4. Train the model: python train_example_vit.py --imgs="./pneumothorax_binary_classification_task_data/" --name="pneumo_model_vit" --epochs=1
5. Rename <MODEL_PATH>/config.json to <MODEL_PATH>/preprocessor_config.json in my case, the model is situated at the output path like ./out/MYVITMODEL/1_2021-08-10-00-53-58/model/
6. Make a prediction: python predict.py --img="42.png" --path="./out/MYVITMODEL/1_2021-08-10-00-53-58/model/"

Models recipes

You can find all the currently available models or tasks under the recipes/ folder.

<table> <tr> <td rowspan="3" width="160"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/pneumothorax.png" width="256"> </td> <td rowspan="3"> <b>Training a Transformer Image Classifier to help radiologists detect Pneumothorax cases:</b> A demonstration of how to train a Image Classifier Transformer model that can distinguish people with or without Pneumothorax thanks to their radiography with HugsVision. </td> <td align="center" width="80"> <a href="https://nbviewer.jupyter.org/github/qanastek/HugsVision/blob/main/recipes/pneumothorax/binary_classification/Image_Classifier.ipynb"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/nbviewer_logo.svg" height="34"> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/qanastek/HugsVision/tree/main/recipes/pneumothorax/binary_classification/Image_Classifier.ipynb"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/github_logo.png" height="32"> </a> </td> </tr> <tr> <td align="center"> <a href="https://colab.research.google.com/drive/1IIs3iWaVcH3sRkijdsXqQit0XXewJ0pJ?usp=sharing"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/colab_logo.png" height="28"> </a> </td> </tr> <!-- ------------------------------------------------------------------- --> <tr> <td rowspan="3" width="160"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/new_blood_cells_coco.png" width="256"> </td> <td rowspan="3"> <b>Training a End-To-End Object Detection with Transformers to detect blood cells:</b> A demonstration of how to train a E2E Object Detection Transformer model which can detect and identify blood cells with HugsVision. </td> <td align="center" width="80"> <a href="https://nbviewer.jupyter.org/github/qanastek/HugsVision/blob/main/recipes/blood_cells/object_detection/Object_Detection.ipynb"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/nbviewer_logo.svg" height="34"> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/qanastek/HugsVision/tree/main/recipes/blood_cells/object_detection/Object_Detection.ipynb"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/github_logo.png" height="32"> </a> </td> </tr> <tr> <td align="center"> <a href="https://colab.research.google.com/drive/1Q7_HYfZKrQJHV052OCGnZBHwKMIep3kv?usp=sharing"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/colab_logo.png" height="28"> </a> </td> </tr> <!-- ------------------------------------------------------------------- --> <tr> <td rowspan="4" width="160"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/kvasir_v2.png" width="256"> </td> <td rowspan="4"> <b>Training a Transformer Image Classifier to help endoscopists:</b> A demonstration of how to train a Image Classifier Transformer model that can help endoscopists to automate detection of various anatomical landmarks, phatological findings or endoscopic procedures in the gastrointestinal tract with HugsVision. </td> <td align="center" width="80"> <a href="https://nbviewer.jupyter.org/github/qanastek/HugsVision/blob/main/recipes/kvasir_v2/binary_classification/Kvasir_v2_Image_Classifier.ipynb"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/nbviewer_logo.svg" height="34"> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/qanastek/HugsVision/blob/main/recipes/kvasir_v2/binary_classification/Kvasir_v2_Image_Classifier.ipynb"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/github_logo.png" height="32"> </a> </td> </tr> <tr> <td align="center"> <a href="https://colab.research.google.com/drive/1PMV-5c54ZlyoVh6dtkazaDdJR7I8VaqN?usp=sharing"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/colab_logo.png" height="28"> </a> </td> </tr> <tr> <td align="center"> <a href="https://medium.com/@yanis.labrak/how-to-train-a-custom-vision-transformer-vit-image-classifier-to-help-endoscopists-in-under-5-min-2e7e4110a353"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/medium.png" height="28"> </a> </td> </tr> <!-- ------------------------------------------------------------------- --> <tr> <td rowspan="3" width="160"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/HAM10000.png" width="256"> </td> <td rowspan="3"> <b>Training and using a TorchVision Image Classifier in 5 min to identify skin cancer:</b> A fast and easy tutorial to train a TorchVision Image Classifier that can help dermatologist in their identification procedures Melanoma cases with HugsVision and HAM10000 dataset. </td> <td align="center" width="80"> <a href="https://nbviewer.jupyter.org/github/qanastek/HugsVision/blob/main/recipes/HAM10000/binary_classification/HAM10000_Image_Classifier.ipynb"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/nbviewer_logo.svg" height="34"> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/qanastek/HugsVision/blob/main/recipes/HAM10000/binary_classification/HAM10000_Image_Classifier.ipynb"> <img src="https://raw.githubusercontent.com/qanastek/HugsVision/main/ressources/images/receipes/github_logo.png" height="32"> </a> </td> </tr> <tr