This repository contains the source code for training computer vision models for different tasks, including ImageNet-1k/21k classification, MS-COCO object detection, ADE20k semantic segmentation, and Kinetics-400 video classification.
CVNets can be installed in the local python environment using the below command:
git clone git@github.com:apple/ml-cvnets.git
cd ml-cvnets
pip install -r requirements.txt
pip install --editable .
We recommend to use Python 3.7+ and PyTorch (version >= v1.8.0) with conda environment. For setting-up python environment with conda, see here.
- General instructions for working with CVNets are given here.
- Examples for training and evaluating models are provided here.
- Examples for converting a PyTorch model to CoreML are provided here.
For benchmarking results including novel and existing models, see Model Zoo.
CVNets is currently maintained by Sachin Mehta and Farzad Abdolhosseini.
We welcome PRs from the community! You can find information about contributing to CVNets in our contributing document.
Please remember to follow our Code of Conduct.
For license details, see LICENSE.
If you find CVNets useful, please cite following papers:
@article{mehta2021mobilevit,
title={MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer},
author={Mehta, Sachin and Rastegari, Mohammad},
journal={arXiv preprint arXiv:2110.02178},
year={2021}
}
@article{mehta2022cvnets,
title={CVNets: High Performance Library for Computer Vision},
author={Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad},
year={2022}
}