Instructions to use timm/deit_tiny_patch16_224.fb_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/deit_tiny_patch16_224.fb_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/deit_tiny_patch16_224.fb_in1k", pretrained=True) - Transformers
How to use timm/deit_tiny_patch16_224.fb_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/deit_tiny_patch16_224.fb_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/deit_tiny_patch16_224.fb_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4eac6cf5d57320298d43abc285666fbd4d2863edbe49511711cb0b58f76969df
- Size of remote file:
- 22.9 MB
- SHA256:
- 5efe4ac9543f5fc5e1e3443ef52b8f39c7e579ca9c031f62ac8d3aa184b21f6c
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