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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("esunn/bread") model = AutoModelForImageClassification.from_pretrained("esunn/bread", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download images/dessert.jpg from esunn/bread: direct link, hf CLI and curl.
- Browser
- Download file 15.5 kB
-
https://huggingface.co/esunn/bread/resolve/main/images/dessert.jpg
- Command line
-
hf download hf://esunn/bread/images/dessert.jpg
-
curl -L -o dessert.jpg https://huggingface.co/esunn/bread/resolve/main/images/dessert.jpg
15.5 kB

- Xet hash:
- 67ebc92e4306a4549f6284082fea4afb6234a8bd3923202e659aad1497ceaa29
- Size of remote file:
- 15.5 kB
- SHA256:
- 1c9af690f1d3cd6f4bedd0d8b81e4c130d6cc4ca273c3c2a9c22dd0915696725
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