Model Optimization: Quantization and Pruning

An implementation and comparison of model compression techniques for ResNet architectures in PyTorch, covering post-training quantization, pruning, and quantization-aware training (QAT).

The project examines the practical trade-offs between model size, inference cost, and predictive performance, with QAT recovering accuracy that naive post-training quantization gives up. It complements earlier edge-computing work on emotion recognition and face recognition deployed to resource-constrained hardware.

Efficiency work of this kind matters wherever deployment constraints sit alongside representation quality, from mobile inference to training and serving large video models.