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--- |
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license: cc-by-4.0 |
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datasets: |
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- imagenet-1k |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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language: |
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- en |
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tags: |
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- resnet |
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- convolutional neural network |
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- simpool |
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- dino |
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- computer vision |
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- deep learning |
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--- |
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# Self-supervised ResNet-50 model |
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ResNet-50 official model trained on ImageNet-1k for 100 epochs. Self-supervision with [DINO](https://arxiv.org/abs/2104.14294). Reproduced for ICCV 2023 [SimPool](https://arxiv.org/abs/2309.06891) paper. |
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SimPool is a simple attention-based pooling method at the end of network, released in this [repository](https://github.com/billpsomas/simpool/). |
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Disclaimer: This model card is written by the author of SimPool, i.e. [Bill Psomas](http://users.ntua.gr/psomasbill/). |
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## Evaluation with k-NN |
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| k | top1 | top5 | |
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| ------- | ------- | ------- | |
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| 10 | 61.84 | 80.35 | |
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| 20 | 62.174 | 82.75 | |
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| 100 | 60.088 | 84.216 | |
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| 200 | 58.544 | 83.834 | |
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## BibTeX entry and citation info |
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``` |
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@misc{psomas2023simpool, |
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title={Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?}, |
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author={Bill Psomas and Ioannis Kakogeorgiou and Konstantinos Karantzalos and Yannis Avrithis}, |
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year={2023}, |
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eprint={2309.06891}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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``` |
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@inproceedings{caron2021emerging, |
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title={Emerging properties in self-supervised vision transformers}, |
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author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J{\'e}gou, Herv{\'e} and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand}, |
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booktitle={Proceedings of the IEEE/CVF international conference on computer vision}, |
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pages={9650--9660}, |
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year={2021} |
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} |
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``` |
