Pytorch-Wildlife v1.0.2

We have just updated Pytorch-Wildlife to version 1.0.2. Although this is a relatively minor release in terms of the package itself, it is a big update with the addition of a module many have requested: the classification fine-tuning module.

In the Pytorch-Wildlife package, we've introduced an additional default classification model trained on the Snapshot Serengeti camera trap dataset. This is the first camera trap classification model we've trained for the African region, in collaboration with Snapshot Safari under Zooniverse. Furthermore, Pytorch-Wildlife is now citable using BibTeX. We are also working on a brief technical report to allow Pytorch-Wildlife to be cited as a paper.

For the classification fine-tuning module, we are currently making it independent of the base package to preserve code-base clarity. This module provides functions that utilize the MegaDetector under Pytorch-Wildlife to pre-crop input camera trap images. It includes data splitting functions based on random split, location split, and shooting sequence split, alongside the entire classification training pipeline. This module produces classification weights that can be directly linked back to other Pytorch-Wildlife functionalities, such as the Gradio demo app. Currently, the classification fine-tuning module is based on ResNet. However, users are more than welcome to employ any classification architecture they prefer, provided some adjustments are made to adhere to the separate feature encoder + classifier convention required by the Pytorch-Wildlife framework. We chose this convention to simplify future utilities and analyses, such as embedding space visualization.

Please do not hesitate to submit any issues you may encounter, enhancement PRs that you think may be good additions, and let us know what you think!

Thank you!