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name: classification description: "Use MambaVision for image classification, feature extraction, pretrained checkpoint handling, ImageNet validation command construction, and safe inference or throughput smokes." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MambaVision Classification

Use this sub-skill when a task is about the mambavision Python package API for ImageNet-style image classification, pooled feature extraction, pretrained checkpoint cache behavior, no-download dummy inference, ImageNet validation command construction, or lightweight throughput checks.

Route here for

• Installing the classification package dependencies and importing from mambavision import create_model.
• Creating any registered factory: mamba_vision_T, mamba_vision_T2, mamba_vision_S, mamba_vision_B, mamba_vision_B_21k, mamba_vision_L, mamba_vision_L_21k, mamba_vision_L2, mamba_vision_L2_512_21k, mamba_vision_L3_256_21k, or mamba_vision_L3_512_21k.
• Running no-download inference smoke tests with arbitrary image height/width.
• Loading local checkpoints, intentionally triggering pretrained downloads, or diagnosing cache/model-path behavior.
• Building ImageNet/ImageFolder validation commands and optional CSV/JSON result output.
• Running a safer local throughput/FLOPs helper without using the original benchmark script.

Route elsewhere

• ImageNet training, fine-tuning, YAML config tuning, distributed launch, AMP/EMA/MESA, and resume behavior: read ../training/SKILL.md.
• Cascade Mask R-CNN / COCO / MMDetection workflows: read ../object-detection/SKILL.md.
• UPerNet / ADE20K / MMSegmentation workflows: read ../semantic-segmentation/SKILL.md.

Do not claim that the OpenMMLab stack is installed from this sub-skill. Classification package verification covered the base MambaVision package and CUDA model forward smoke only; downstream detection/segmentation dependencies are optional and routed to their own sub-skills.

First steps

1. Check the package/import contract in references/api-reference.md.
2. Pick the factory and checkpoint family from references/model-overview.md.
3. For no-download API validation, run scripts/smoke_mambavision_inference.py --help and then run it with --pretrained omitted.
4. For ImageNet or custom ImageFolder validation, use references/validation-workflows.md to construct a command and verify dataset/checkpoint prerequisites before launching.
5. For benchmark questions, prefer scripts/benchmark_mambavision.py over the original throughput workflow because it fixes the batch-size variable bug, guards CUDA use, and makes FLOPs optional.
6. If installation, CUDA, checkpoint, data, shape, or timm warning issues appear, consult references/troubleshooting.md.

Bundled helper guarantees

• Safe defaults: no downloads, no training, no dataset writes, no credentials, and tiny batch sizes by default.
• --pretrained is opt-in; without it, helpers call create_model(..., pretrained=False).
• The smoke helper asserts finite logits and the expected output shape.
• The benchmark helper reports measured latency/throughput and skips optional FLOPs unless requested.