Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use esunn/bread with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esunn/bread with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="esunn/bread") 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("esunn/bread") model = AutoModelForImageClassification.from_pretrained("esunn/bread") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b19e72b785c60df77042f03477d61301598af46932dea07a9ede31593ca409b
- Size of remote file:
- 343 MB
- SHA256:
- f48785dfcf858d4efa6e842b866569edb79be36a487a031e6c4ddc400393f047
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