---

name: "demo-visualize" description: "Guides AdelaiDet image/video/webcam demos, VisualizationDemo usage, confidence thresholds, output handling, and dataset visualization." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

demo-visualize

Use this sub-skill when a task asks to run AdelaiDet inference on images, videos, webcam streams, visualize predictions, adjust confidence thresholds, or inspect dataset annotations visually.

Use this route for

• Building AdelaiDet demo commands for image, video, or webcam inference.
• Understanding VisualizationDemo, Detectron2 predictors, and output behavior.
• Running dataset visualization before training.
• Debugging missing MODEL.WEIGHTS, input/output path, OpenCV, or headless-display issues.
• Visualizing text/non-text predictions after setup succeeds.

Do not use this route for

• Environment/build failures. Use ../setup-build/SKILL.md.
• Training/evaluation launches. Use ../train-eval/SKILL.md.
• Text lexicons/dictionaries or text evaluator behavior. Use ../text-spotting/SKILL.md.
• Dataset conversion/preparation. Use ../data-prep/SKILL.md.
• ONNX export. Use ../export-convert/SKILL.md.

Read first

• references/demo-workflows.md for image/video/webcam launch patterns.
• references/visualization.md for dataset visualization and output interpretation.
• ../../references/troubleshooting.md for OpenCV/headless and checkpoint issues.

Skill-owned scripts

• scripts/run_demo.py — validates repo root, config, weights, input/output mode, then builds/runs the demo command.
• scripts/visualize_dataset.py — validates and launches the dataset visualization workflow.

Typical workflow

1. Confirm setup with ../../scripts/check_install.py --cuda-ops.

2. Choose a config and weights. Use train-eval if you are not sure which config family fits.

3. Dry-run the demo:

   python scripts/run_demo.py --repo-root /path/to/AdelaiDet \
     --config configs/FCOS-Detection/R_50_1x.yaml \
     --weights /path/to/model.pth --input image.jpg --output outputs/ --dry-run
   

4. Remove --dry-run to execute.

5. For dataset visual inspection, use scripts/visualize_dataset.py --dry-run first.

Decision points

• Headless server with no display: use image/video input and --output; avoid webcam or GUI window modes.
• Text model predictions: use this route for drawing outputs, but switch to text-spotting for lexicon/evaluation semantics.
• Missing/incorrect boxes or masks with a custom dataset: switch to data-prep to inspect annotations and dataset mappers.