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Model card for vit_large_patch14_clip_336.laion2b_ft_augreg_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.

Run Validation

python validate.py /tfds/ --dataset tfds/i_naturalist2021 --model hf-hub:timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21 --split val --amp


  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},
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