How to Train a Neural Network like a Boss

A developer shares how they trained a computer vision neural network to identify pool balls across diverse environments using synthetic data generated in Blender. Rather than manually labeling thousands of real photos, they used Blender renders with varied lighting, camera distortions, and ball positions to auto-generate labeled training data. To speed up the 10,000-render generation and model training process, they used cloud GPU compute via modal.com, completing in 5-6 hours for $80 what would have taken days locally. The resulting model generalized successfully to untrained environments, demonstrating the power of synthetic data for robust computer vision applications.