ML Image Classification
ResNet-50 Fine-Tuning & Interpretability
Improved model accuracy from 81% to 94.2% through systematic fine-tuning and data augmentation.
Context
This project demonstrates core machine learning engineering discipline — systematic experimentation, interpretability through Grad-CAM visualizations, and measurable improvement through principled model optimization.
The problem
Baseline model accuracy was 81%, insufficient for production use. The challenge was improving accuracy through principled techniques while maintaining interpretability — understanding not just whether the model was right, but why.
How I built it
Implemented targeted data augmentation based on failure analysis of misclassified samples
Applied cosine learning-rate scheduling for stable convergence
Used class-weighted loss to handle class imbalance without oversampling
Added strategic dropout regularization to prevent overfitting
Built Grad-CAM visualization pipeline for model interpretability and failure diagnosis
Why these choices
Grad-CAM for interpretability
Accuracy metrics alone don't reveal whether a model is learning meaningful features. Grad-CAM visualizations showed where the model was attending, enabling targeted improvements.
Transfer learning with selective unfreezing
Full fine-tuning risks catastrophic forgetting on small datasets. Selective unfreezing of later layers preserved learned features while adapting to the target domain.