Instructions to use facebook/deit-tiny-distilled-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/deit-tiny-distilled-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/deit-tiny-distilled-patch16-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("facebook/deit-tiny-distilled-patch16-224") model = AutoModelForImageClassification.from_pretrained("facebook/deit-tiny-distilled-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/deit-tiny-distilled-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 23.7 MB
-
https://huggingface.co/facebook/deit-tiny-distilled-patch16-224/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/deit-tiny-distilled-patch16-224/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/deit-tiny-distilled-patch16-224/resolve/main/pytorch_model.bin
23.7 MB
- Xet hash:
- adf4423560fce9a19f52fe0b00355ff7a048f9f641ecad3a62ef6e548f67ebeb
- Size of remote file:
- 23.7 MB
- SHA256:
- deb997894af550bd9e9c7f1951a564c7df613108f7687085c4de7110b041f198
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