Instructions to use aalonso-developer/vit-base-clothing-leafs-example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aalonso-developer/vit-base-clothing-leafs-example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aalonso-developer/vit-base-clothing-leafs-example") 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("aalonso-developer/vit-base-clothing-leafs-example") model = AutoModelForImageClassification.from_pretrained("aalonso-developer/vit-base-clothing-leafs-example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aalonso-developer/vit-base-clothing-leafs-example: direct link, hf CLI and curl.
- Browser
- Download file 361 MB
-
https://huggingface.co/aalonso-developer/vit-base-clothing-leafs-example/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aalonso-developer/vit-base-clothing-leafs-example/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aalonso-developer/vit-base-clothing-leafs-example/resolve/main/pytorch_model.bin
361 MB
- Xet hash:
- bfd8d0660b151be9384b6ce2493b2ee8eb615df66c658c136efc8163908407d5
- Size of remote file:
- 361 MB
- SHA256:
- 1e7b928a23e6cb66b71030426d53a9fbca942ccdf322ec06c776c5fade7d066c
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