Image Classification
Transformers
TensorBoard
Safetensors
vit
food
fruits
junkfood
Generated from Trainer
Instructions to use gutkia01/vit-food-classification-gutkia01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gutkia01/vit-food-classification-gutkia01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gutkia01/vit-food-classification-gutkia01") 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("gutkia01/vit-food-classification-gutkia01") model = AutoModelForImageClassification.from_pretrained("gutkia01/vit-food-classification-gutkia01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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metrics:
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- accuracy
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model-index:
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- name: vit-food-classification-
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results: []
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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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# vit-food-classification-
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food-classification dataset.
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It achieves the following results on the evaluation set:
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metrics:
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- accuracy
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model-index:
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- name: vit-food-classification-gutkia01
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results: []
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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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should probably proofread and complete it, then remove this comment. -->
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# vit-food-classification-gutkia01
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food-classification dataset.
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It achieves the following results on the evaluation set:
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