Model card for convnext_large_mlp.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.
| Model | Top-1 | Top-5 | Img Size (Train) | Paper |
|---|---|---|---|---|
| eva02_large_patch14_clip_336.merged2b_ft_inat21 | 92.05 | 98.01 | 336 | https://arxiv.org/abs/2303.11331 |
| vit_large_patch14_clip_336.datacompxl_ft_augreg_inat21 | 91.98 | 98.03 | 336 | https://arxiv.org/abs/2304.14108 |
| vit_large_patch14_clip_336.laion2b_ft_augreg_inat21 | 91.48 | 97.89 | 336 | https://arxiv.org/abs/2212.07143 |
| convnext_large_mlp.laion2b_ft_augreg_inat21 | 90.95 | 97.68 | 448 (384) | |
| vit_large_patch14_clip_336.datacompxl_ft_inat21 | 90.85 | 97.68 | 336 | https://arxiv.org/abs/2304.14108 |
| convnext_large_mlp.laion2b_ft_augreg_inat21 | 90.62 | 97.61 | 384 | |
| vit_large_patch14_clip_336.laion2b_ft_in12k_in1k_inat21 | 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}
}