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Monitoring School Meal Quality - 1
·
Module 3
Image Data Preparation & Annotation Strategy
1
Image Preprocessing with OpenCV and PIL
Perform essential image preprocessing using OpenCV and PIL: loading, resizing, and color space conversion.
2
Data Augmentation with Torchvision Transforms
Define and apply a set of data augmentation transforms (e.g., rotation, flipping, color jitter) using `torchvision.transforms`.
3
Designing an Annotation Schema for Meal Quality
Define a clear annotation schema for meal quality, quantity, and violations.
4
Image Annotation Basics
Use a lightweight annotation tool or script to label a small subset of images according to the schema.
5
Custom PyTorch Dataset for Image Annotation and Transformations
Build a custom PyTorch `Dataset` class to load annotated images and apply transformations for model training.
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Data Acquisition & Exploratory Analysis
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Baseline 1: Image-Only Meal Quality Classification