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Monitoring School Meal Quality - 1
·
Module 4
Baseline 1: Image-Only Meal Quality Classification
1
Convolutional Neural Network (CNN) Fundamentals
Explain the roles of convolutional layers, pooling layers, and feature maps at a conceptual level.
2
Fine-tuning ResNet-50 for Meal Quality Classification
Load a pretrained CNN (e.g., ResNet-50) from `torchvision` and adapt its final layer for the custom meal quality task.
3
GPU-Accelerated CNN Fine-Tuning for Meal Image Analysis
Fine-tune the pretrained CNN on the annotated meal images, leveraging GPU acceleration.
4
Handling Imbalanced Classes with Weighted Cross-Entropy
Implement a strategy to handle class imbalance, such as using weighted cross-entropy loss.
5
Evaluating Image Classifier Performance
Evaluate the image classifier's performance using metrics like precision, recall, F1-score, and a confusion matrix.
Previous module
Image Data Preparation & Annotation Strategy
Next module
Baseline 2: Text-Only Complaint Classification