Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use MDZN/fruit-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MDZN/fruit-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MDZN/fruit-classifier") 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("MDZN/fruit-classifier") model = AutoModelForImageClassification.from_pretrained("MDZN/fruit-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5dc0ab017ce5efc2b3137b35de7a0cdb47063b9f6c553bd5b897157a10ec60f
- Size of remote file:
- 343 MB
- SHA256:
- 1b5949c174c5722b1b83f998974413b8cab7ec64791ad82b778f0aafc9c1bfc2
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