Instructions to use timm/vit_pe_spatial_tiny_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_tiny_patch16_512.fb with timm:
import timm model = timm.create_model("hf_hub:timm/vit_pe_spatial_tiny_patch16_512.fb", pretrained=True) - Transformers
How to use timm/vit_pe_spatial_tiny_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_tiny_patch16_512.fb")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_pe_spatial_tiny_patch16_512.fb", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fc1cdc9ce85e587b365f9f69ad5cb941b3f85305b8df04e1f752d523c47e3f2
- Size of remote file:
- 22.8 MB
- SHA256:
- 36570c7c677dfeb9757140181a33c890e8eac6b50e457073383e52dd0678568a
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