What's New
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In this version of Pytorch-Wildlife, we are happy to release our detection fine-tuning module, with which users can fine-tune their own detection model from any released pre-trained MegaDetectorV6 models. Besides, this module also has functionalities that help users to prepare their datasets for the fine-tuning, just as our classification fine-tuning modules. For more details, please check the readme. Currently the fine-tuning is based on Ultralytics with AGPL. We will release MIT versions in the future.
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We have also released additional MegaDetectorV6 models based on Yolo-v10 and RtDetr. We have skipped Yolo-v11 models because of limited performance and architectural gains. In the following figure we can see the Parameter to Performance metrics of our released models. Users can choose according to specific project needs. All of the MegaDetectorV6 models have at least 50% less parameters than MegaDetectorV5 and still have better animal detection performance. Our smallest released model is MegaDetectorV6-yolov10-c, which only has 2% of the parameter size of MegaDetectorV5 but still has 4% better animal detection recall.
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In terms of the MIT and Apache versions, most of them have also finished training but are waiting for internal review before they can be released.
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We have also updated our AI4G-Amazon model with bigger datasets and it has a better performance compared to previous iterations. Please feel free to test it or fine-tune on it.
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We will also make a new roadmap for 2025 in the next couple of updates.