CompressAI-Vision helps you to develop, test and evaluate compression models with standardized tests in the context of compression methods optimized for machine tasks algorithms such as Neural-Network (NN)-based detectors.
It currently focuses on two types of pipeline:
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Video compression for remote analysis, which corresponds to the MPEG "Video Coding for Machines" (VCM) activity.
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Split inference, which includes an evaluation framework for compressing intermediate features produced in the context of split models. The software supports all thepipelines considered in the related MPEG activity: "Feature Compression for Machines" (FCM).
The figure below shows the split inference pipeline:
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Detectron2 is used for object detection (Faster-RCNN) and instance segmentation (Mask-RCNN)
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JDE is used for Object Tracking
A complete documentation is provided here, including installation, CLI usage, as well as tutorials.
To get started locally and install the development version of CompressAI-Vision, first create a virtual environment with python==3.8:
python3.8 -m venv venv
source ./venv/bin/activate
pip install -U pip
The CompressAI library providing learned compresion modules is available as a submodule. It can be initilized by running:
git submodule update --init --recursive
To install the models relevant for the FCM (feature compression):
First, if you want to manually export CUDA related paths, please source (e.g. for CUDA 11.8):
bash scripts/env_cuda.sh 11.8
Then, run:, please run:
bash scripts/install.sh
For more otions, check:
bash scritps/install.sh --help
For split-inference pipelines, after installing, please use the following entry point:
compressai-vision-eval --help
For example for full pipelines without any compression, run
compressai-vision-eval --config-name=eval_example
Please check other configuration examples provided in ./cfgs as well as examplary scripts in ./scripts
Test data related to the MPEG FCM activity can be found in ./data/mpeg-fcm/
After your dev, you can run (and adapt) test scripts from the scripts/tests directory. Please check scripts/tests/Readme.md for more details
Code is formatted using black and isort. To format code, type:
make code-format
Static checks with those same code formatters can be run manually with:
make static-analysis
Tutorials are produced from notebooks that are in docs/source/tutorials. If you update the notebooks, first you need to run compile.bash therein.
To produce the html documentation, from docs/, run:
make html
To check the pages locally, open docs/_build/html/index.html
CompressAI-Vision is licensed under the BSD 3-Clause Clear License
Fabien Racapé, Hyomin Choi, Eimran Eimon, Sampsa Riikonen, Jacky Yat-Hong Lam