gabrielilharco/snap-n-eat
Food detection and recommendation with deep learning
Combines ResNeXt-101 finetuned on Food-101 (achieving 71% accuracy) with nutritional recommendation via n-dimensional distance in nutrient space, then surfaces nearby restaurants serving suggested meals. Built on PyTorch/fastai for model training and Flask/Node.js for the web application, with microservices handling prediction, feature extraction, and meal ranking.
289 stars. No commits in the last 6 months.
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289
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74
Language
Jupyter Notebook
License
MIT
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Last pushed
Mar 01, 2023
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