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 pytorch_model.bin 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/08b32640f8012e55af6a4a94357623b2fcab515b/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k@08b32640f8012e55af6a4a94357623b2fcab515b/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k/resolve/08b32640f8012e55af6a4a94357623b2fcab515b/pytorch_model.bin
279 MB
- Xet hash:
- 4fa1680d7f43f38975b16a72bc430fdc0ff453a1e4dd7f788a84ac2541627f8d
- Size of remote file:
- 279 MB
- SHA256:
- 5455ec771428ef5ef3bd774d42e56e563b01f748191e031a198b546d15417017
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