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") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- c1e6d335bb09b98272d17f32225a21c385d9d748610f88b212bbcd59af657540
- Size of remote file:
- 29.6 kB
- SHA256:
- be8b544677879fa4eedc627b4d78b5c9eaf720c41c7d82d427e4ef1285ecd703
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