Duplicate from AmazonScience/MILE

Co-authored-by: Alin Popa <popaaln@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-nc-4.0
+datasets:
+- abo
+- inaturalist
+pipeline_tag: image-feature-extraction
+---
+
+
+## Model training and evaluation
+
+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).
+
+## Intended use and limitations
+
+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.
+
+ * [ABO](https://huggingface.co/AmazonScience/MILE/resolve/main/blrp-dinov2-vitl14-reg-lora0-fw-5e-06-mt09996-bs3-800_ckpt0390.pth)
+ * [iNaturalist](https://huggingface.co/AmazonScience/MILE/resolve/main/mile-vitl14-reg-LRx8-g48x8-100M-mt0996-bs3-s250000-ep800-1_ckpt0361.pth)
+
+
+## Citation
+If you use this work, please cite:
+
+```
+@inproceedings{leotescu2024mile,
+  title={Self-Supervised Incremental Learning of Object Representations from Arbitrary Image Sets},
+  author={Leotescu, George and Popa, Alin-Ionut Popa and Grigore, Diana and Voinea, Daniel and Perona, Pietro},
+  booktitle={Proceedings of WACV},
+  year={2025}
+}
+```
+
+## License
+
+This library is licensed under the CC BY NC License.

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