---

name: semantic-segmentation description: "Use MambaVision as an MMSegmentation UPerNet backbone on ADE20K, including config selection, checkpoint adaptation, data layout, single-GPU or Slurm command planning, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MambaVision Semantic Segmentation

Use this sub-skill when the task is about the published MambaVision UPerNet semantic segmentation workflow on ADE20K.

It covers:

• choosing the tiny, small, base, or L3 config
• adapting backbone checkpoint paths and decoder channels
• preparing ADE20K in the expected folder layout
• generating safe single-GPU train/test commands or adapted Slurm launch patterns
• diagnosing registry, import, channel, crop-size, AMP, and dataset layout failures

Route elsewhere:

• classification checkpoints, ImageNet validation, or the model catalog -> ../classification/SKILL.md
• COCO detection or Cascade Mask R-CNN workflows -> ../object-detection/SKILL.md
• generic MMSegmentation utilities, dataset converters, or deployment helpers are upstream framework topics and are not selected workflows here

Start with:

1. references/configuration.md to choose the config, checkpoint family, crop size, and optimizer wrapper.
2. references/workflows.md for single-GPU and Slurm train/test command patterns.
3. references/backbone-adapter.md for MM_mamba_vision registration, output channels, and checkpoint loading behavior.
4. references/troubleshooting.md when imports, ADE20K paths, checkpoint shapes, or AMP settings fail.
5. scripts/print_mmseg_command.py --help to see the safe command builder surface.

Expected runtime signals:

• The selected MMSegmentation test entry point reports aAcc, mIoU, and mAcc for ADE20K validation.
• The published ADE20K results recorded in references/configuration.md are 46.0 / 48.2 / 49.1 / 53.2 mIoU for tiny / small / base / L3.

Prerequisites:

• A CUDA-enabled PyTorch + OpenMMLab stack with mmengine==0.10.1, mmcv==2.1.0, opencv-python-headless, mmsegmentation==1.2.2, mmdet==3.3.0, and mmpretrain==1.2.0.
• The ADE20K dataset tree under ADEChallengeData2016 with images/training, images/validation, annotations/training, and annotations/validation.
• The segmentation CLI in the target project must be able to import the MM_mamba_vision adapter; add the target project's adapter directory to PYTHONPATH if the framework registry cannot find it.