Instructions to use BabyChou/vit-base-patch16-imagenette with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BabyChou/vit-base-patch16-imagenette with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BabyChou/vit-base-patch16-imagenette") 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("BabyChou/vit-base-patch16-imagenette") model = AutoModelForImageClassification.from_pretrained("BabyChou/vit-base-patch16-imagenette", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from BabyChou/vit-base-patch16-imagenette: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/BabyChou/vit-base-patch16-imagenette/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://BabyChou/vit-base-patch16-imagenette/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/BabyChou/vit-base-patch16-imagenette/resolve/main/flax_model.msgpack
343 MB
- Xet hash:
- 2b31b9c9e0594f0f5708c8df709b914a24256c0aa8f77a13b351211d7de74698
- Size of remote file:
- 343 MB
- SHA256:
- c0aba8753f7adc2a9327d71b680046c5fbfba7aee575650778f476c2b28860aa
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