---

name: "mambavision" description: "Routes MambaVision users to classification, ImageNet training, and OpenMMLab backbone-adaptation workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MambaVision repo skill

Use this skill when a request involves the MambaVision repository, the mambavision package, published ImageNet checkpoints, ImageNet training or fine-tuning, or the bundled MMDetection and MMSegmentation adapters.

MambaVision is a hybrid Mamba/Transformer vision backbone. This root skill routes you to the smallest sub-skill that matches the task and points you at the shared environment and provenance notes needed before you start.

First checks

1. Read references/repo-provenance.md when you need to know whether this skill matches the current checkout.
2. Run scripts/check_mambavision_env.py --help to inspect the environment-check helper surface.
3. Use scripts/check_mambavision_env.py --smoke for a no-download import and forward check after installing the base package.
4. Read references/installation.md before asking for package installs, wheel selection, or rebuild steps.
5. Read references/troubleshooting.md when imports, checkpoint loading, registry wiring, or backend support fails.

Install and verify

The verified package version for this generated skill is mambavision==1.2.0. The base package needs torch, timm, transformers, mamba-ssm, einops, requests, Pillow, and tensorboardX. Downstream object-detection and semantic-segmentation workflows additionally need the matching OpenMMLab stack. See references/installation.md for the exact package sets and smoke commands.

Route map

classification

Use this sub-skill for image classification, factory selection, no-download inference smoke tests, pretrained checkpoint handling, ImageNet validation planning, and safe throughput benchmarking.

Read:

• sub-skills/classification/SKILL.md
• sub-skills/classification/references/api-reference.md
• sub-skills/classification/references/model-overview.md
• sub-skills/classification/references/validation-workflows.md
• sub-skills/classification/references/troubleshooting.md

Typical tasks:

• choose a backbone factory or checkpoint family
• build an inference smoke command
• validate a local checkpoint against an ImageFolder tree
• compare safe throughput options

training

Use this sub-skill for ImageNet training and fine-tuning, YAML preset selection, distributed launch planning, EMA/MESA flags, checkpoint resume, and data-layout checks.

Read:

• sub-skills/training/SKILL.md
• sub-skills/training/references/training-workflows.md
• sub-skills/training/references/configuration.md
• sub-skills/training/references/data-formats.md
• sub-skills/training/references/troubleshooting.md

Typical tasks:

• adapt torchrun or single-GPU launch templates
• choose the right preset and model family
• debug OOM, resume, and validation-loop behavior

object-detection

Use this sub-skill for the published COCO Cascade Mask R-CNN workflows that attach MambaVision as an MMDetection backbone.

Read:

• sub-skills/object-detection/SKILL.md
• sub-skills/object-detection/references/configuration.md
• sub-skills/object-detection/references/backbone-adapter.md
• sub-skills/object-detection/references/workflows.md
• sub-skills/object-detection/references/troubleshooting.md

Typical tasks:

• adapt backbone and detector checkpoints
• print safe single-GPU train/test commands
• debug MM_mamba_vision, COCO layout, and metric selection

semantic-segmentation

Use this sub-skill for the published ADE20K UPerNet workflows that attach MambaVision as an MMSegmentation backbone.

Read:

• sub-skills/semantic-segmentation/SKILL.md
• sub-skills/semantic-segmentation/references/configuration.md
• sub-skills/semantic-segmentation/references/backbone-adapter.md
• sub-skills/semantic-segmentation/references/workflows.md
• sub-skills/semantic-segmentation/references/troubleshooting.md

Typical tasks:

• choose the tiny, small, base, or L3 recipe
• print safe train/test command templates
• debug MM_mamba_vision, ADE20K layout, crop size, and AMP behavior

Shared helpers

• scripts/check_mambavision_env.py checks import readiness, optional OpenMMLab imports, and an opt-in no-download forward smoke.
• references/repo-routing-metadata.json is the structured scenario map used by the router.
• references/repo-provenance.md captures the repo snapshot and construction baseline.

Operating boundaries

• Do not start long training jobs, dataset conversions, or checkpoint downloads unless the user explicitly asks for execution and accepts the cost.
• Treat the bundled command helpers as templates; they do not launch anything.
• Keep runtime links inside this skill tree. If a task needs a deeper workflow, route to the matching sub-skill instead of reopening the source checkout.