Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use nickmuchi/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickmuchi/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nickmuchi/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("nickmuchi/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("nickmuchi/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on beans datasetโ
Beep boop, I am a bot from Hugging Face's automatic model evaluator ๐!\ Your model has been evaluated on the [beans](https://huggingface.co/datasets/beans) dataset by @lewtun, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-ac4402f5-7985079).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=beans).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=beans).e9fa390
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Update README.mdโ
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update model card README.md
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๐ป cheers
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Model save
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Training in progress, step 500
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initial commit
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