Instructions to use timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k with timm:
import timm model = timm.create_model("hf-hub:timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k", pretrained=True) - Transformers
How to use timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k") 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_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k: direct link, hf CLI and curl.
- Browser
- Download file 279 MB
-
https://huggingface.co/timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k/resolve/ff44d09e711acba2ddcfa77672bdd1e0364cde46/model.safetensors
- Command line
-
hf download hf://timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k@ff44d09e711acba2ddcfa77672bdd1e0364cde46/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k/resolve/ff44d09e711acba2ddcfa77672bdd1e0364cde46/model.safetensors
279 MB
- Xet hash:
- 46a1af49b709d4b92688719fb7dc74f6b6d8d41456b5b5a39bf7124b9a1a1011
- Size of remote file:
- 279 MB
- SHA256:
- 6300353437f2c8062e8725b7ffd136f6e2e10090e7810bb685e631eeb9d08e6f
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