It’s been a while since I last posted a new entry on the TorchVision memoirs series. Thought, I’ve previously shared news on the official PyTorch blog and on Twitter, I thought it would be a good idea to talk more about what happened on the last release of TorchVision (v0.12), what’s coming out on the next one (v0.13) and what are our plans for 2022H2. My target is to go beyond providing an overview of new features and rather provide insights on where we want to take the project in the following months.
TorchVision v0.12 was a sizable release with dual focus: a) update our deprecation and model contribution policies to improve transparency and attract more community contributors and b) double down on our modernization efforts by adding popular new model architectures, datasets and ML techniques.
Key for a successful open-source project is maintaining a healthy, active community that contributes to it and drives it forwards. Thus an important goal for our team is to increase the number of community contributions, with the long term vision of enabling the community to contribute big features (new models, ML techniques, etc) on top of the usual incremental improvements (bug/doc fixes, small features etc).