Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use RohithN2004/fruitripenessv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RohithN2004/fruitripenessv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="RohithN2004/fruitripenessv2") 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/fruitripenessv2") model = AutoModelForImageClassification.from_pretrained("RohithN2004/fruitripenessv2") - Notebooks
- Google Colab
- Kaggle
fruitripenessv2
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
FreshApple
FreshBanana
FreshGrape
FreshGuava
FreshJujube
FreshOrange
FreshPomegranate
FreshStrawberry
RottenApple
RottenBanana
RottenGrape
RottenGuava
RottenJujube
RottenOrange
RottenPomegranate
RottenStrawberry
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Evaluation results
- Accuracyself-reported0.998















