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")# pip install -U transformers accelerate # 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
Download images/Grape.jpg from MDZN/fruit-classifier: direct link, hf CLI and curl.
- Browser
- Download file 26.9 kB
-
https://huggingface.co/MDZN/fruit-classifier/resolve/main/images/Grape.jpg
- Command line
-
hf download hf://MDZN/fruit-classifier/images/Grape.jpg
-
curl -L -o Grape.jpg https://huggingface.co/MDZN/fruit-classifier/resolve/main/images/Grape.jpg
26.9 kB

- Xet hash:
- 4a825627fdad5339a2ec901775ac1eb6cd8a541f29b48ba26da9b60dedd393f5
- Size of remote file:
- 26.9 kB
- SHA256:
- 24f4db6b63337b6dfaea06af37d4b33a9fb19d252e6e2d1de64d81a35eef1346
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