Image Classification
Transformers
PyTorch
TensorBoard
vit
other-image-classification
Generated from Trainer
Eval Results (legacy)
Instructions to use nateraw/vit-base-beans-demo-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/vit-base-beans-demo-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/vit-base-beans-demo-v2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nateraw/vit-base-beans-demo-v2") model = AutoModelForImageClassification.from_pretrained("nateraw/vit-base-beans-demo-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nateraw/vit-base-beans-demo-v2: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/nateraw/vit-base-beans-demo-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nateraw/vit-base-beans-demo-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nateraw/vit-base-beans-demo-v2/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- b08e3b6c39e9513a91e7abe8ecd0e15dee64059206b5876add7186667502eaab
- Size of remote file:
- 343 MB
- SHA256:
- 8f9141e450b863b1b71e627cf128f40987b6cb648796eb7bf7e3bca5e56ec3b5
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