Instructions to use timm/deit_small_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_small_patch16_224.fb_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/deit_small_patch16_224.fb_in1k", pretrained=True) - Transformers
How to use timm/deit_small_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_small_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_small_patch16_224.fb_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f2d91267367807d27583d1ea17ffe9d3a0e53f4b1b3d4edfa27ecb58cfe77147
- Size of remote file:
- 88.3 MB
- SHA256:
- 52fa3d9d746c967a02a6765fa31368eb481d52cea269798a4a33c172f1c03242
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