Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use nateraw/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/vit-base-beans") 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("nateraw/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("nateraw/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nateraw/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 2.67 kB
-
https://huggingface.co/nateraw/vit-base-beans/resolve/main/training_args.bin
- Command line
-
hf download hf://nateraw/vit-base-beans/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nateraw/vit-base-beans/resolve/main/training_args.bin
2.67 kB
- Xet hash:
- 514c22bb562c4cbfa9cbe3d530da246d4c8474ecec03f27487f2c661087938e4
- Size of remote file:
- 2.67 kB
- SHA256:
- dbfebeca32be1d102a71d29619d0db0783df335f73af946dd488cfac20c644ec
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