Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
huggingpics
Eval Results (legacy)
Instructions to use Bazaar/cv_apple_leaf_disease_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bazaar/cv_apple_leaf_disease_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Bazaar/cv_apple_leaf_disease_detection") 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("Bazaar/cv_apple_leaf_disease_detection") model = AutoModelForImageClassification.from_pretrained("Bazaar/cv_apple_leaf_disease_detection") - Notebooks
- Google Colab
- Kaggle
cv_apple_leaf_disease_detection
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
Alternaria leaf spot
Brown spot
Frogeye leaf spot
Grey spot
Health
Mosaic
Powdery mildew
Rust
Scab
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Spaces using Bazaar/cv_apple_leaf_disease_detection 2
Evaluation results
- Accuracyself-reported0.976








