ml-cvnets's Introduction

CVNets: A library for training computer vision networks

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.

Installation

CVNets can be installed in the local python environment using the below command:

    git clone [email protected]: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.

Getting Started

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.

Model Zoo

For benchmarking results including novel and existing models, see Model Zoo.

The Team

CVNets is currently maintained by Sachin Mehta and Farzad Abdolhosseini.

Contributing

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.

License

For license details, see LICENSE.

Citation

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}
}

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