This repository provides a library and tools for training and deploying CNN models (RotNet, RotNetR) to predict the rotation angle of images, primarily designed for cracking rotate captchas. The system includes a training pipeline with customizable dataset preprocessing (random rotations, cropping, normalization), model definitions using ResNet50 and RegNet_Y backbones, a loss function optimized for angular classification (Circular Smooth Label), and an HTTP server for inference. The codebase is well-structured as a Python package with clean separation of concerns, showcasing advanced patterns like generic pipe operators for data transformation composition and context-managed training loops. It is actively maintained and demonstrates solid software engineering practices.