Model Compression with Keras Pruning and Post-Training Quantization

This talks about the techniques used to compress a model as well as improve the inference latency: training a baseline model, fine-tuning it with magnitude-based weight pruning to high sparsity, and then exporting a deployment-ready TensorFlow Lite (TFLite) model using post-training quantization (PTQ). Using the tutorial pipeline, we obtain an export model sparsity of 0.799 and achieve an 8.99× reduction in model size (78,283 bytes to 8,703 bytes) with a modest test accuracy decrease (0.9785 to 0.9669).