Instructions to use facebook/deit-base-distilled-patch16-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/deit-base-distilled-patch16-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/deit-base-distilled-patch16-384") 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-distilled-patch16-384") model = AutoModelForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-384", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/deit-base-distilled-patch16-384: direct link, hf CLI and curl.
- Browser
- Download file 351 MB
-
https://huggingface.co/facebook/deit-base-distilled-patch16-384/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/deit-base-distilled-patch16-384/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/deit-base-distilled-patch16-384/resolve/main/pytorch_model.bin
351 MB
- Xet hash:
- 7e73837f127277d0370253a2f31dda2cd10fe4896672a79636df297363e1e062
- Size of remote file:
- 351 MB
- SHA256:
- dec73721e660733aa027e42bdb0e9e3ee6cbbaf05bf97965ce32d38c04f2e4e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.