Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use RohithN2004/fruit-ripeness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RohithN2004/fruit-ripeness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="RohithN2004/fruit-ripeness") 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("RohithN2004/fruit-ripeness") model = AutoModelForImageClassification.from_pretrained("RohithN2004/fruit-ripeness") - Notebooks
- Google Colab
- Kaggle
fruit-ripeness
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
ripe apple
ripe mango
ripe papaya
ripe pomegranate
rotten apple
rotten mango
rotten papaya
rotten pomegranate
unripe apple
unripe mango
unripe papaya
unripe pomegranate
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Evaluation results
- Accuracyself-reported0.285











