Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use nateraw/food with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/food with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/food") 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("nateraw/food") model = AutoModelForImageClassification.from_pretrained("nateraw/food", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72ea200660784b6d610b199c329dfde614107020e176103cfbe9617e4db25ffa
- Size of remote file:
- 2.67 kB
- SHA256:
- 854337aa979b4d66fa7b7c37eeeed817ea454fd96fce3eeddd27799e56c2a3d1
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