jfzhang95/pytorch-deeplab-xception

Health v2 · maintenance only

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3000 stars · 770 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of the DeepLab v3+ semantic segmentation model with support for multiple backbones (Xception, ResNet, MobileNet, DRN). It includes training and evaluation scripts for Pascal VOC, SBD, Cityscapes, and COCO datasets with multi-GPU support.

Use cases

• train a DeepLab v3+ semantic segmentation model in PyTorch
• segment images with pretrained DeepLab v3+ weights on Pascal VOC
• swap segmentation backbones like ResNet, Xception, or MobileNetV2
• train semantic segmentation on Cityscapes or COCO datasets
• reproduce DeepLab v3+ mIoU benchmark results
• fine-tune a segmentation model with focal loss or balanced class weights

When to choose

• you need a well-known, benchmarked DeepLab v3+ PyTorch implementation with pretrained weights
• you want to experiment with different segmentation backbones and datasets
• you need multi-GPU training for semantic segmentation

When to avoid

• you need actively maintained code compatible with recent PyTorch versions
• you want the latest segmentation architectures like SegFormer or Mask2Former
• you need production-ready inference serving rather than research training code

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision deep-learning machine-learning image-processing python cross-platform semantic-segmentation deeplab-v3-plus pytorch xception resnet mobilenetv2 pretrained-models pascal-voc cityscapes gpu linux

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