---

name: mmsegmentation description: "Use MMSegmentation for semantic segmentation configs, datasets, inference, training, evaluation, model customization, registries, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMSegmentation

Use this repo skill when a task involves OpenMMLab MMSegmentation: semantic segmentation or depth configs, model zoo recipes, datasets and transforms, inference, training/testing, metrics, custom models, registries, checkpoints, or OpenMMLab dependency errors.

Start Here

1. Confirm the installed package imports:

   python -c "import mmseg, mmcv, mmengine; print(mmseg.__version__, mmcv.__version__, mmengine.__version__)"
   

2. Read install-and-compatibility if the task touches setup, dependency pins, CPU/CUDA, mmcv, torch, numpy, OpenCV, or optional project dependencies.

3. Run check_mmseg_env.py for a deterministic import/version/backend check before blaming user code.

4. Route to the focused sub-skill below.

Route By Task

• Inference and visualization: read inference for MMSegInferencer, init_model, image/video/batch inference, SegDataSample, headless output, saved masks, remote-sensing tiled inference, and deployment caveats.
• Configs, datasets, and transforms: read data-configuration for MMEngine config inheritance, --cfg-options, packaged config families, dataset layouts, BaseSegDataset, transforms, class palettes, and dataset-conversion patterns.
• Training, testing, and metrics: read training-evaluation for safe train/test command construction, Runner.from_cfg, resume vs fine-tune, IoUMetric, DepthMetric, TTA, output formatting, distributed launch, Slurm, and log analysis.
• Models, registries, and checkpoints: read model-customization for built-in components, custom backbones/heads/losses/metrics, MODELS/METRICS, custom_imports, project extensions, checkpoint conversion, FLOPs caveats, and registry debugging.

Common Decisions

• Prefer adapting an existing model-zoo config over writing a full model dict from scratch.
• Prefer local config and checkpoint files for deterministic inference; model aliases can trigger checkpoint downloads.
• Treat training, testing, benchmarks, dataset conversion, checkpoint conversion, and distributed jobs as side-effectful; use bundled dry-run wrappers first and execute only after approval.
• Keep default_scope = 'mmseg' unless intentionally composing with another OpenMMLab package.
• Use CPU for inspection and small smoke checks; document CUDA/NPU/other backends as runtime choices that need matching torch/mmcv builds.

Shared References

• repo-provenance records the source snapshot and evidence baseline for refresh decisions.
• repo-routing-metadata is used by DisCo's managed router during import.
• install-and-compatibility covers dependency and backend compatibility.
• troubleshooting covers cross-cutting install/import/backend/config/data failures.

Bundled Helpers

• scripts/check_mmseg_env.py: imports core modules, checks versions, optional CUDA visibility, and warns about common incompatible numpy/OpenCV/torch/MMCV combinations.
• Sub-skill helpers provide safe inference, config inspection, dataset layout checking, dry-run train/test commands, log analysis, and registry inspection.

Do Not

• Do not tell future agents to run original checkout scripts or open original docs as part of this skill. Use the bundled references and scripts here.
• Do not launch long training, distributed jobs, benchmarks, downloads, or dataset conversion without user approval.
• Do not assume mmcv-lite can run all model/API paths; model components that use compiled ops may require full mmcv matched to torch and the backend.
• Do not treat random-weight inference or a no-checkpoint model build as meaningful segmentation output.