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name: inference-demo description: "Guides FCOS image inference, installed CLI use, public FCOS API calls, image preprocessing, visualization, and demo troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

FCOS Inference and Demo

Use this sub-skill when the user wants to run FCOS on images, inspect fcos.FCOS, use the installed fcos command, choose a pretrained model, format detection outputs, or debug demo/inference failures.

Start here

1. Read references/api-reference.md for the public FCOS constructor and method contracts.
2. Read references/model-overview.md before selecting a pretrained high-level API model or a config/weight pair.
3. Read references/workflows.md for CLI, Python API, no-display, and webcam/display workflows.
4. Use scripts/prepare_image_for_fcos.py to validate a local image and produce a resized BGR NumPy array for API tests.
5. Use scripts/fcos_cli_safe_wrapper.py to construct or run a no-display FCOS inference path without relying on GUI behavior.
6. Read references/troubleshooting.md for _C, downloads, color-channel, display, and OOM failures.

Boundaries

• Route config selection, dataset registration, and YAML errors to ../data-configs/SKILL.md.
• Route COCO/VOC/Cityscapes evaluation and training commands to ../training-evaluation/SKILL.md.
• Route ONNX export or ONNX post-processing to ../onnx-export/SKILL.md.
• Route source edits, compiled layer tests, or modern PyTorch porting to ../internals-maintenance/SKILL.md.

Minimal safe response pattern

When a user asks for image inference:

1. Ask whether model weights may be downloaded if they did not provide a local weight file.
2. Prefer no-display output in automated contexts; only call visualization when a display is available.
3. Verify the package and extension before model construction because FCOS(...) loads weights and builds a detector.
4. Ensure the input is a 3-channel image. The installed CLI reads RGB via image I/O, flips to BGR, and resizes the shorter side to 800.
5. Set cpu_only=True only for CPU runs; expect it to be slow.

Output contract reminder

FCOS.detect(im) returns a list of dictionaries with box, score, label_name, and label_id. Boxes are [x1, y1, x2, y2] floats in the image coordinate system used for inference.