vision transformer simpool dino computer vision deep learning image-classification en dataset:imagenet-1k arxiv:2104.14294 arxiv:2309.06891 license:cc-by-4.0 region:us
---
license: cc-by-4.0
datasets:
- imagenet-1k
metrics:
- accuracy
pipeline_tag: image-classification
language:
- en
tags:
- vision transformer
- simpool
- dino
- computer vision
- deep learning
---

# Self-supervised ViT-S/16 (small-sized Vision Transformer with patch size 16) model

ViT-S 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      | 68.918  | 85.432  |
| 20      | 68.738  | 87.278  |
| 100     | 66.746  | 88.52   |
| 200     | 65.33   | 88.26   |


## 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{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}
}
```