Instructions to use ewanlong/food_type_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ewanlong/food_type_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ewanlong/food_type_image_detection") 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("ewanlong/food_type_image_detection") model = AutoModelForImageClassification.from_pretrained("ewanlong/food_type_image_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- Optimizer: AdamW
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- Epochs: 20
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- Model Performance: Achieved an accuracy of 96.23% on all of the kinds of Food Image Classification Dataset
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### Dataset Card
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- Dataset Name: 'Food Image Classification Dataset'
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- Dataset Size: 24K unique images
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- Dataset Description: 35 varieties of both Indian and Western appetizers
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- Dataset Source: The unique images obtained from various Google resources by Kaggle user Harish Kumar
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- Dataset License: CC0: Public Domain
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- Dataset Quality:
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- Meticulously curated images ensuring diversity and representativeness
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- Provides a solid foundation for developing robust and precise image classification algorithms
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- Encourages exploration in the fascinating field of food image classification)
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- Expected Update Frequency: Quarterly
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- Optimizer: AdamW
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- Epochs: 20
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- Model Performance: Achieved an accuracy of 96.23% on all of the kinds of Food Image Classification Dataset
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