pytorch-semantic-segmentation and pytorch-segmentation
These are **competitors** — both are standalone PyTorch implementations of semantic segmentation models with similar scope, and users would typically choose one based on which repository's model architectures, dataset support, and loss functions better match their specific needs.
About pytorch-semantic-segmentation
zijundeng/pytorch-semantic-segmentation
PyTorch for Semantic Segmentation
About pytorch-segmentation
yassouali/pytorch-segmentation
:art: Semantic segmentation models, datasets and losses implemented in PyTorch.
Implements multiple encoder-decoder architectures (DeepLab V3+, PSPNet, UperNet, U-Net, etc.) with atrous convolution and multi-scale parsing strategies for dense pixel-level prediction. Provides specialized loss functions including Lovász-Softmax for direct mIoU optimization and focal loss for handling class imbalance, alongside poly and one-cycle learning rate schedulers commonly used in segmentation workflows. Supports Pascal VOC, Cityscapes, ADE20K, and COCO Stuff datasets with JSON-based configuration for reproducible training pipelines.
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