Instructions to use timm/vit_mediumd_patch16_reg4_gap_256.sbb_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.sbb_in12k with timm:
import timm model = timm.create_model("hf_hub:timm/vit_mediumd_patch16_reg4_gap_256.sbb_in12k", pretrained=True) - Transformers
How to use timm/vit_mediumd_patch16_reg4_gap_256.sbb_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.sbb_in12k") 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_mediumd_patch16_reg4_gap_256.sbb_in12k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7932170149b31b3531e3607a8b304a46b59f2f616feab78896ffb5a7aa23f581
- Size of remote file:
- 279 MB
- SHA256:
- e161de3128e9a07e5800aeabbaa6efeafd38e786b8df936c3706b2e8be6fd117
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