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 A hands-on lab comparing post-training quantization, magnitude pruning, and knowledge distillation on small CNNs.
 
-![Python](https://img.shields.io/badge/Python-3.10%2B-3776AB?logo=python&logoColor=white) ![PyTorch](https://img.shields.io/badge/PyTorch-2.5-EE4C2C?logo=pytorch&logoColor=white) ![License](https://img.shields.io/badge/License-MIT-green)
+[![CI](https://github.com/Mathos34/cnn-compression-lab/actions/workflows/ci.yml/badge.svg)](https://github.com/Mathos34/cnn-compression-lab/actions/workflows/ci.yml) ![Python](https://img.shields.io/badge/Python-3.10%2B-3776AB?logo=python&logoColor=white) ![PyTorch](https://img.shields.io/badge/PyTorch-2.5-EE4C2C?logo=pytorch&logoColor=white) ![License](https://img.shields.io/badge/License-MIT-green)
 
 ![result](assets/result.png)