pytorch-segmentation

Why we included this project

Training semantic segmentation models often means juggling several repos for models, losses, and data handling. This project pulls the common pieces into one place: DeepLab V3+, PSPNet, UperNet, and a few other encoders, plus the loss functions, learning-rate schedules, data augmentations, and dataset loaders those models typically rely on. It is built as a training toolkit rather than a deployable service, with JSON config files that make swapping models or tuning hyperparameters straightforward. Newcomers get everything pre-wired, while experienced users can read a single architecture's implementation without digging through unrelated code. The included Colab notebook also lets you run a full training loop in the browser before you commit local GPU time.