DEEP LEARNING
CompressAI — PyTorch Library for Data Compression Models
CompressAI is an open-source library and platform for evaluating data compression models
The tool is based on the PyTorch deep learning framework. The task of data compression is to minimize the size of the data and at the same time preserve information as much as possible.
More about the library
Currently, CompressAI provides:
- Custom operations, layers, and neural network models for data compression;
- Part of the functionality from the official TensorFlow library for compression;
- Pretrained end-to-end compression models for image compression tasks;
- Scripts for evaluating and comparing trained models with classical methods of compressing photo and video data
The current version of the library does not support multi-GPU training. However, the developers plan to add this functionality in future versions of the library.
Available DL Models
CompressAI’s neural networks were trained in 4–5 million iterations on 256-by-256 image patches that were accidentally extracted and cropped from images in the Vimeo90K dataset. The models were trained with a batch size of 16 or 32. On average, to achieve state-of-the-art quality, the models were trained from one to two weeks, depending on the GPU used.
Multiple architectures from the state-of-the-art on learned image compression have been re-implemented in PyTorch with the domain specific modules and layers provided by CompressAI. There are 6 architectures currently available in CompressAI:
One of the most important features of CompressAI is the ability to easily implement deep neural networks for end-to-end compression. Several domain-specific layers, operations, and modules have been implemented on top of PyTorch, such as entropy models, quantization operations, color transforms.
Future work
Currently, CompressAI tools and documentation mostly focus on learned image compression and will soon add support for video compression. However, other end-to-end compression pipelines could be built using CompressAI, like the compression of 3D maps or deep features for example.
The developers have also published tutorials on working with the library in the official repository on GitHub.
Original Paper
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