Welcome to Segmentation Models’s documentation!# Contents: ⚙️ Installation 🚀 Quick Start 🕸️ Segmentation Models Unet Unet++ FPN PSPNet DeepLabV3 DeepLabV3+ Linknet MAnet PAN UPerNet Segformer 🔍 Available Encoders ResNet ResNeXt ResNeSt Res2Ne(X)t RegNet(x/y) GERNet SE-Net SK-ResNe(X)t DenseNet Inception EfficientNet MobileNet DPN VGG Mix Visual Transformer MobileOne 🎯 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
