This project contains the training code for the Microsoft AI for Earth Species Classification API, along with the code for our API demo page. This API classifies handheld photos of around 5000 plant and animal species. There is also a pipeline included for training detectors, and an API layer that simplifies running inference with an existing model, either on whole images or on detected crops.
The training data is not provided in this repo, so you can think of this repo as a set of tools for training fine-grained classifiers. If you want lots of animal-related data to play around with, check out our open data repository at lila.science, including LILA's list of other data sets related to conservation.
This repository is licensed with the MIT license.
The FasterRCNNDetection directory is based on https://github.com/chenyuntc/simple-faster-rcnn-pytorch.
The PyTorchClassification directory is based on the ImageNet example from the PyTorch codebase.
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