See-Sense investigates the accuracy of AI-driven carbohydrate estimation to support Type 1 Diabetes (T1D) management.
- How effectively can computer vision models (EfficientNetB0) identify food items and provide accurate carbohydrate values for insulin dosing, when audited against clinical safety buckets?
By building a Gold Standard dataset and optimizing models for consumer-grade hardware (e.g., RTX 3050), this project aims to reduce the mental burden of carb counting for the T1D community.
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Improve classification accuracy (EfficientNetB0 tuning or new backbones)
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Experiment with regularization, augmentation, or learning-rate strategies
- Identify imposter images
- Remove corrupted or mislabeled data
- Preserve Gold dataset integrity
- Improve clinical carb bucketing logic:
- Keto
- Low
- Medium
- High
- Test Mixed Precision (FP16)
- Evaluate VRAM limits on different GPUs
- Improve
live_demo.py
To protect the Gold dataset and the main model, always work on a new branch.
# Create and switch to a new branch
git checkout -b feature-improve-accuracy
# Push the branch to GitHub
git push origin feature-improve-accuracy-
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Using the Command Line:
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Open Terminal: Open your terminal or command prompt.
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Navigate to Your Repository: Use cd to navigate to your local repository:
cd /path/to/your/repo -
Create and Switch to New Branch:
git checkout -b new-branch-name(Name your branch after your challenge) -
Push the New Branch to GitHub: Push the new branch to GitHub so it is available on the remote repository.
git push origin new-branch-name(Name your branch after your challenge) -
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git branch
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