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--- |
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license: cc-by-nc-4.0 |
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datasets: |
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- abo |
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- inaturalist |
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pipeline_tag: image-feature-extraction |
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--- |
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## Model training and evaluation |
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Instructions for how to train and evaluate a MILE model, as well as the necessary code are in the [Amazon Science repository](https://github.com/amazon-science/mile). |
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## Intended use and limitations |
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These models have been trained on [Amazon-Berkeley-Objects](https://amazon-berkeley-objects.s3.amazonaws.com/index.html) and [iNaturalist](https://github.com/visipedia/inat_comp/tree/master/2017) and are intended to demonstrate the power of the object-level embeddings w.r.t. the object /category retrieval task. For other domains or tasks, it should be further fine-tuned on relevant data. |
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* [ABO](https://huggingface.co./AmazonScience/MILE/resolve/main/blrp-dinov2-vitl14-reg-lora0-fw-5e-06-mt09996-bs3-800_ckpt0390.pth) |
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* [iNaturalist](https://huggingface.co./AmazonScience/MILE/resolve/main/mile-vitl14-reg-LRx8-g48x8-100M-mt0996-bs3-s250000-ep800-1_ckpt0361.pth) |
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## Citation |
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If you use this work, please cite: |
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``` |
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@inproceedings{leotescu2024mile, |
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title={Self-Supervised Incremental Learning of Object Representations from Arbitrary Image Sets}, |
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author={Leotescu, George and Popa, Alin-Ionut Popa and Grigore, Diana and Voinea, Daniel and Perona, Pietro}, |
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booktitle={Proceedings of WACV}, |
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year={2025} |
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} |
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``` |
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## License |
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This library is licensed under the CC BY NC License. |
