vision - Comprehensive Computer Vision Tools: Datasets, Models, and Transformations

Introduction to TorchVision

TorchVision is an essential library for computer vision enthusiasts and professionals. It is part of the PyTorch ecosystem and provides a rich collection of popular datasets, model architectures, and image transformation tools. This makes it a valuable asset for anyone working on computer vision projects, whether they are just getting started or are experienced veterans in the field.

Installation

Installing TorchVision is straightforward. Detailed installation instructions can be found on the official PyTorch website. The library supports various Python versions, from 3.9 to 3.12, depending on the version being installed. For those looking to contribute or build from the source, the contributing page offers guidance.

Image and Video Backends

TorchVision offers support for several image backends, including:

Torch Tensors: Native support within PyTorch.
PIL Images: Utilizes the Python Imaging Library and includes a faster variant called Pillow-SIMD, which is a drop-in replacement for Pillow that uses SIMD for enhanced performance.

For video processing, TorchVision provides support through:

PyAV: A Python binding for ffmpeg libraries that acts as the default video backend.
Video Reader: Requires ffmpeg to be installed without conflicting versions and supports only Linux systems when built from source.

Using TorchVision Models in C++

TorchVision also extends its functionalities to C++ through the libtorchvision library. It includes custom operations and many C++ APIs. However, users should be cautious as these APIs may change between versions. To maintain stability in a C++ environment, it is advisable to use the Python APIs, exporting them via torchscript.

Documentation and Contribution

Comprehensive API documentation is available on the PyTorch website. For those interested in contributing to TorchVision, instructions are provided in the CONTRIBUTING file available in the project repository.

Datasets and Licensing

TorchVision serves as a utility library for downloading and preparing public datasets. However, it does not manage or host these datasets. Users must verify whether they have the necessary permissions under the datasets’ licenses before usage. Dataset owners wishing to update or remove their datasets from the library are encouraged to do so via a GitHub issue.

Similarly, pre-trained models in TorchVision come with licenses or terms based on the datasets used during training. Users must ensure compliance with these conditions for their use cases. Notably, models such as the SWAG models are released under the CC-BY-NC 4.0 license.

Citing TorchVision

For individuals who find TorchVision valuable in their work, citing the library can be done using the following BibTeX entry:

@software{torchvision2016,
    title        = {TorchVision: PyTorch's Computer Vision library},
    author       = {TorchVision maintainers and contributors},
    year         = 2016,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/pytorch/vision}}
}

TorchVision is not just a library but a robust tool that empowers those involved in the field of computer vision by providing the resources required to elevate research and development projects.