Instructions to use victorwkey/vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use victorwkey/vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="victorwkey/vit_model") 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("victorwkey/vit_model") model = AutoModelForImageClassification.from_pretrained("victorwkey/vit_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from victorwkey/vit_model: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/victorwkey/vit_model/resolve/main/training_args.bin
- Command line
-
hf download hf://victorwkey/vit_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/victorwkey/vit_model/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 8937343f232787e40bad95e15c54edc44468c9cc789af6c29863347840e93503
- Size of remote file:
- 5.37 kB
- SHA256:
- b7d2d11268becac572f99bc0e07cd73a3e9b35f6f403aa2b86f00a04fa0bd029
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