--- license: cc-by-4.0 datasets: - imagenet-1k metrics: - accuracy pipeline_tag: image-classification language: - en tags: - convnext - convolutional neural network - simpool - dino - computer vision - deep learning --- # Self-supervised ConvNeXt-S model [ConvNeXt-S](https://arxiv.org/abs/2201.03545) 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. SimPool is a simple attention-based pooling method at the end of network, released in this [repository](https://github.com/billpsomas/simpool/). Disclaimer: This model card is written by the author of SimPool, i.e. [Bill Psomas](http://users.ntua.gr/psomasbill/). ## Evaluation with k-NN | k | top1 | top5 | | ------- | ------- | ------- | | 10 | 59.342 | 80.058 | | 20 | 59.224 | 82.252 | | 100 | 56.468 | 83.256 | | 200 | 54.878 | 82.754 | ## BibTeX entry and citation info ``` @misc{psomas2023simpool, title={Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?}, author={Bill Psomas and Ioannis Kakogeorgiou and Konstantinos Karantzalos and Yannis Avrithis}, year={2023}, eprint={2309.06891}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ``` @inproceedings{liu2022convnet, title={A convnet for the 2020s}, author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining}, booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, pages={11976--11986}, year={2022} } ``` ``` @inproceedings{caron2021emerging, title={Emerging properties in self-supervised vision transformers}, author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J{\'e}gou, Herv{\'e} and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand}, booktitle={Proceedings of the IEEE/CVF international conference on computer vision}, pages={9650--9660}, year={2021} } ```