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
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("esunn/bread")
model = AutoModelForImageClassification.from_pretrained("esunn/bread")Quick Links
bread
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
Example Images
bread
dessert
- Downloads last month
- 9
Evaluation results
- Accuracyself-reported0.867


# 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")