Instructions to use timm/vit_base_patch16_rope_reg1_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_base_patch16_rope_reg1_gap_256.sbb_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/vit_base_patch16_rope_reg1_gap_256.sbb_in1k", pretrained=True) - Transformers
How to use timm/vit_base_patch16_rope_reg1_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_base_patch16_rope_reg1_gap_256.sbb_in1k") 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_base_patch16_rope_reg1_gap_256.sbb_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d5b66143a6c01ab48707cb0567d9494339f9f6371f03f1411818627f46e2e60
- Size of remote file:
- 346 MB
- SHA256:
- 80b312781474989302c9ed3e8d603acf91c26cb725ef0cb37252a5d05172c0f3
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