Instructions to use facebook/deit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/deit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/deit-base-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-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("facebook/deit-base-patch16-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/deit-base-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/deit-base-patch16-224/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/pytorch_model.bin
346 MB
- Xet hash:
- deb26ac5b920594f82abb782e0aadea228f06a3e23756666080b79bc0ba2985b
- Size of remote file:
- 346 MB
- SHA256:
- 9272c1b6a10a5792cfc29423e463f162f6f9293be0f763abc0ec5cbd7ce34294
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