Instructions to use timm/vit_pe_spatial_small_patch16_512.fb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_pe_spatial_small_patch16_512.fb with timm:
import timm model = timm.create_model("hf_hub:timm/vit_pe_spatial_small_patch16_512.fb", pretrained=True) - Transformers
How to use timm/vit_pe_spatial_small_patch16_512.fb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_pe_spatial_small_patch16_512.fb")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_pe_spatial_small_patch16_512.fb", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f297304faafdfe451d5793ce7a6fa47cb7285f9ff96cc2ba952cd7f7ab262e1e
- Size of remote file:
- 88 MB
- SHA256:
- 102e21a0730e7aeea8b8e7ea553cbbcb9f3dbec5dbd84f5ed7d9d3ec3b421016
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