Instructions to use timm/vit_medium_patch16_reg4_gap_256.sbb_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_medium_patch16_reg4_gap_256.sbb_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/vit_medium_patch16_reg4_gap_256.sbb_in1k", pretrained=True) - Transformers
How to use timm/vit_medium_patch16_reg4_gap_256.sbb_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_medium_patch16_reg4_gap_256.sbb_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_medium_patch16_reg4_gap_256.sbb_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from timm/vit_medium_patch16_reg4_gap_256.sbb_in1k: direct link, hf CLI and curl.
- Browser
- Download file 156 MB
-
https://huggingface.co/timm/vit_medium_patch16_reg4_gap_256.sbb_in1k/resolve/main/model.safetensors
- Command line
-
hf download hf://timm/vit_medium_patch16_reg4_gap_256.sbb_in1k/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/timm/vit_medium_patch16_reg4_gap_256.sbb_in1k/resolve/main/model.safetensors
156 MB
- Xet hash:
- 7721427359909395979ae1715c507905d90e44a8dac7eff76de0fd95026a1788
- Size of remote file:
- 156 MB
- SHA256:
- 261cad8b586d0c78f95548f7e0fec6b394016bdcc6862278427161f032ad54cb
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