CoreNet is a deep neural network toolkit that allows researchers and engineers to train standard and novel small and large-scale models for variety of tasks, including foundation models (e.g., CLIP and LLM), object classification, object detection, and semantic segmentation.

• April 2024: Version 0.1.0 of the CoreNet library includes
  • OpenELM
  • CatLIP
  • MLX examples

Below is the list of publications from Apple that uses CoreNet:

• OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework

You will need Git LFS (instructions below) to run tests and Jupyter notebooks (instructions) in this repository, and to contribute to it so we recommend that you install and activate it first.

On Linux we recommend to use Python 3.10+ and PyTorch (version >= v2.1.0), on macOS system Python 3.9+ should be sufficient.

Note that the optional dependencies listed below are required if you'd like to make contributions and/or run tests.

For Linux (substitute apt for your package manager):

sudo apt install git-lfs

git clone [email protected]:apple/corenet.git
cd corenet
git lfs install
git lfs pull
# The following venv command is optional, but recommended. Alternatively, you can create and activate a conda environment.
python3 -m venv venv && source venv/bin/activate
python3 -m pip install --editable .

To install optional dependencies for audio and video processing:

sudo apt install libsox-dev ffmpeg

For macOS, assuming you use Homebrew:

brew install git-lfs

git clone [email protected]:apple/corenet.git
cd corenet
cd \$(pwd -P)  # See the note below.
git lfs install
git lfs pull
# The following venv command is optional, but recommended. Alternatively, you can create and activate a conda environment.
python3 -m venv venv && source venv/bin/activate
python3 -m pip install --editable .

To install optional dependencies for audio and video processing:

brew install sox ffmpeg

Note that on macOS the file system is case insensitive, and case sensitivity can cause issues with Git. You should access the repository on disk as if the path were case sensitive, i.e. with the same capitalization as you see when you list the directories ls. You can switch to such a path with the cd $(pwd -P) command.

This section provides quick access and a brief description for important CoreNet directories.

└── tutorials
    ├── train_a_new_model_on_a_new_dataset_from_scratch.ipynb
    ├── guide_slurm_and_multi_node_training.md
    ├── clip.ipynb
    ├── semantic_segmentation.ipynb
    └── object_detection.ipynb

└── projects
    ├── byteformer
    ├── catlip (*)
    ├── clip
    ├── fastvit
    ├── mobilenet_v1
    ├── mobilenet_v2
    ├── mobilenet_v3
    ├── mobileone
    ├── mobilevit
    ├── mobilevit_v2
    ├── openelm (*)
    ├── range_augment
    ├── resnet
    └── vit


(*) Newly released.

└──mlx_example
    ├── clip
    └── open_elm

└── corenet
    └── modeling
        └── models
            ├── audio_classification
            ├── classification
            ├── detection
            ├── language_modeling
            ├── multi_modal_img_text
            └── segmentation

└── corenet
    └── data
        └── datasets
            ├── audio_classification
            ├── classification
            ├── detection
            ├── language_modeling
            ├── multi_modal_img_text
            └── segmentation

└── corenet
    ├── loss_fn
    ├── metrics
    ├── optims
    │   └── scheduler
    ├── train_eval_pipelines
    ├── data
    │   ├── collate_fns
    │   ├── sampler
    │   ├── text_tokenizer
    │   ├── transforms
    │   └── video_reader
    └── modeling
        ├── layers
        ├── modules
        ├── neural_augmentor
        └── text_encoders

This code is developed by Sachin, and is now maintained by Sachin, Maxwell Horton, Mohammad Sekhavat, and Yanzi Jin.

• Farzad

We welcome PRs from the community! You can find information about contributing to CoreNet in our contributing document.

Please remember to follow our Code of Conduct.

For license details, see LICENSE.

CoreNet evolved from CVNets, to encompass a broader range of applications beyond computer vision. Its expansion facilitated the training of foundational models, including LLMs.

If you find our work useful, please cite the following paper:

@inproceedings{mehta2022cvnets, 
     author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad}, 
     title = {CVNets: High Performance Library for Computer Vision}, 
     year = {2022}, 
     booktitle = {Proceedings of the 30th ACM International Conference on Multimedia}, 
     series = {MM '22} 
}