Instructions to use kinghoon/vit-base-beans-demo-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kinghoon/vit-base-beans-demo-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kinghoon/vit-base-beans-demo-v5") 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("kinghoon/vit-base-beans-demo-v5") model = AutoModelForImageClassification.from_pretrained("kinghoon/vit-base-beans-demo-v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kinghoon/vit-base-beans-demo-v5: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/kinghoon/vit-base-beans-demo-v5/resolve/main/training_args.bin
- Command line
-
hf download hf://kinghoon/vit-base-beans-demo-v5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kinghoon/vit-base-beans-demo-v5/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 050a61f81282c970f1ed364664b270a1ae7ad4f11cf808e193666a4789db5d63
- Size of remote file:
- 5.11 kB
- SHA256:
- 7402d4465ed532b3eba767f1e60e3f1ca1f237c049eb41e795b4ff1e723de0b9
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