Welcome to Segmentation Models’s documentation!# Contents: ⚙️ Installation 🚀 Quick Start 🕸️ Segmentation Models Unet Unet++ FPN PSPNet DeepLabV3 DeepLabV3+ Linknet MAnet PAN UPerNet Segformer DPT 🔍 Available Encoders Choosing the Right Encoder 🎯 Timm Encoders Traditional-Style Transformer-Style 📉 Losses Constants JaccardLoss DiceLoss TverskyLoss FocalLoss LovaszLoss SoftBCEWithLogitsLoss SoftCrossEntropyLoss MCCLoss 📏 Metrics Functional metrics 📂 Saving and Loading Saving and Sharing a Model Loading Trained Model Saving model Metrics and Dataset Name Saving with preprocessing transform (Albumentations) Conclusion 💡 Insights 1. Models architecture 2. Creating your own encoder 3. Aux classification output Indices and tables# Index Module Index Search Page
