Instructions to use timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21 with timm:
import timm model = timm.create_model("hf_hub:timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21", pretrained=True) - Transformers
How to use timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update config
Browse files- config.json +2 -2
config.json
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{
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"architecture": "
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"num_classes": 10000,
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"num_features": 1024,
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"global_pool": "token",
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"Lygodium japonicum": "Japanese climbing fern, Plant"
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},
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"pretrained_cfg": {
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"tag": "
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"custom_load": false,
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"input_size": [
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3,
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{
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"architecture": "vit_large_patch14_clip_336",
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"num_classes": 10000,
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"num_features": 1024,
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"global_pool": "token",
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"Lygodium japonicum": "Japanese climbing fern, Plant"
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},
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"pretrained_cfg": {
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"tag": "laion2b_ft_augreg_inat21",
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"custom_load": false,
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"input_size": [
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3,
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