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", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 2b378dfb8de40247a617e34ebafaa06d9d7d391bb198940207f49ac796c1a132
- Size of remote file:
- 1.24 MB
- SHA256:
- a64d47ef86cbb4e0cfaad551b243e7da42b859da6c4d76856d9f2ee8aa3076a0
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