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
Commit History
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Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#4)
- Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (ba1c4b330d07ba930ed13f9919b3a33eae5c2d76) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>41f85ac
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Add evaluation results on beans dataset (#2)
- Add evaluation results on beans dataset (9cbf311b0d31ebdf2ea58dc820ef0b7620357e2f) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>cecf50f
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initial commit
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