convnext_large_mlp.laion2b_ft_augreg_inat21

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匿名用户2024年07月31日
14阅读
开发技术pytorch
所属分类ai、timm、image-classification
开源地址https://modelscope.cn/models/timm/convnext_large_mlp.laion2b_ft_augreg_inat21
授权协议cc-by-nc-4.0

作品详情

Model card for convnextlargemlp.laion2bftaugreg_inat21

Part of a series of timm fine-tune experiments on iNaturalist 2021 competition data (https://github.com/visipedia/inat_comp/tree/master/2021) for higher capacity models.

Covering 10,000 species, this dataset and these models are fun to explore via the classification widget with pictures from your backyard, but quite a bit smaller than models you can find on iNaturalist website (https://www.inaturalist.org/blog/75633-a-new-computer-vision-model-v2-1-including-1-770-new-taxa).

No extra meta-data was used for training these models (as was the case for the competition), it was a straightfoward fine-tune to explore differences in model pretrain data.

Model Top-1 Top-5 Img Size (Train) Paper
eva02largepatch14clip336.merged2bftinat21 92.05 98.01 336 https://arxiv.org/abs/2303.11331
vitlargepatch14clip336.datacompxlftaugreg_inat21 91.98 98.03 336 https://arxiv.org/abs/2304.14108
vitlargepatch14clip336.laion2bftaugreg_inat21 91.48 97.89 336 https://arxiv.org/abs/2212.07143
convnextlargemlp.laion2bftaugreg_inat21 90.95 97.68 448 (384)
vitlargepatch14clip336.datacompxlftinat21 90.85 97.68 336 https://arxiv.org/abs/2304.14108
convnextlargemlp.laion2bftaugreg_inat21 90.62 97.61 384
vitlargepatch14clip336.laion2bftin12kin1kinat21 90.29 97.44 336 https://arxiv.org/abs/2212.07143

Run Validation

python validate.py /tfds/ --dataset tfds/i_naturalist2021 --model hf-hub:timm/convnext_large_mlp.laion2b_ft_augreg_inat21 --split val --amp

Citation

@inproceedings{cherti2023reproducible,
  title={Reproducible scaling laws for contrastive language-image learning},
  author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2818--2829},
  year={2023}
}
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