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
Update README.md
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README.md
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- food101
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metrics:
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- accuracy
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- name: food101_outputs
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results:
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- task:
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name: nateraw/food101
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type: food101
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args: default
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name: Accuracy
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- food101
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metrics:
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- accuracy
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model-index:
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- name: food101_outputs
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results:
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- task:
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name: nateraw/food101
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type: food101
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8912871287128713
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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