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name: inference-visualization description: "Guide MMYOLO image/video inference, LabelMe export, large-image tiling, feature-map visualization, and CAM analysis." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

MMYOLO inference and visualization

Use this sub-skill when the task is to run or plan MMYOLO inference on images, folders, URLs, or videos; save visualized detections or LabelMe annotations; inspect DetDataSample predictions; tile large images with SAHI-style merging; or create feature-map / BoxAM / Grad-CAM visualizations.

Read order

1. Read references/inference-visualization.md for command recipes, output semantics, and validation checklists.
2. Read references/api-reference.md when the user needs Python API access, DetDataSample.pred_instances, LabelMe writing, or large-image merge internals.
3. Read references/troubleshooting.md for device, dependency, TTA, LabelMe, video, SAHI, and Grad-CAM failures.
4. Use scripts/mmyolo_infer_image_command.py to build a safe self-contained image/folder/URL inference command. The helper only emits a command/template; it does not run inference.

Fast routing

• Image, directory, or URL inference: use the bundled command builder or the API recipe; set --device cpu for a CPU-only run and provide a matching config/checkpoint.
• LabelMe export: use --to-labelme; add --class-name ... only for class-filtered LabelMe shapes. --show and --to-labelme are mutually exclusive.
• Programmatic results: use mmdet.apis.init_detector and inference_detector; read result.pred_instances.bboxes, .scores, and .labels.
• Video / large-image / feature-map / BoxAM: use the reference recipes; they need media files, checkpoints, and sometimes optional packages.

Route away

• Training, testing, AP metrics, prediction JSON/PKL dumps, distributed launch, AMP, or resume -> training-evaluation.
• Dataset conversion, COCO/YOLO/LabelMe data preparation, annotation browsing, or anchor optimization -> data-tools.
• ONNX/TensorRT/RKNN/MMDeploy/EasyDeploy export or inference from backend artifacts -> deployment-conversion.

Operating boundaries

Do not rely on MMYOLO source-checkout demo scripts in runtime commands. Use installed MMYOLO/MMDetection APIs and the bundled helper/templates here. Avoid downloads unless the user explicitly supplied or approved the source URL/checkpoint acquisition.