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name: pytorch-yolo-v3 description: "Route pytorch-yolo-v3 Darknet cfg, image detection, video camera demo, preprocessing, NMS, and legacy PyTorch YOLOv3 troubleshooting tasks." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

pytorch-yolo-v3

Use this repo skill when a user is working with the legacy pytorch-yolo-v3 source repository for PyTorch YOLOv3 object detection: Darknet cfg parsing, local YOLO weights, still-image detection, OpenCV video/camera demos, preprocessing, postprocessing, confidence/NMS behavior, or repository-specific errors.

First checks

• This is a script-oriented source repository, not an installable package in the inspected baseline. Expect a local user checkout/source tree plus Python dependencies rather than package metadata or console entry points.
• The detector needs local Darknet weights for full inference. Do not download yolov3.weights automatically.
• Prefer bundled dry-run or smoke helpers before loading weights, writing detections, opening GUI windows, or touching a camera.
• Training/fine-tuning is out of scope for this baseline; the README says the repository contains the detection module only.

For a safe root preflight, run:

python scripts/check_environment.py
python scripts/check_environment.py --repo-root <repo-root> --check-files

This checks dependencies and optional checkout imports without weights, downloads, GUI, camera, video, or inference.

Route by task

User task or signal	Read
Parse or validate Darknet cfg files; inspect Darknet; understand load_weights/save_weights; debug Something I dunno, unsupported region/reorg, class-count/filter mismatch, or names files	sub-skills/model-and-config/SKILL.md
Run still-image or image-directory detection; build a safe detection command; debug preprocessing, write_results, confidence/NMS, no detections, output naming, image extension handling, or CPU/CUDA image inference	sub-skills/image-detection/SKILL.md
Use or debug video_demo.py, cam_demo.py, or video_demo_half.py; handle OpenCV display/camera/video input, half precision, ignored --video, or headless-server issues	sub-skills/video-camera-demos/SKILL.md
Install/import/dependency issues, missing weights, source-checkout assumptions, CUDA expectations, or cross-cutting stop conditions	references/troubleshooting.md
Decide whether this skill is stale for a current checkout	references/repo-provenance.md

Operating workflow

1. Identify the user's intended workflow: cfg/model inspection, image detection, or video/camera demo.
2. Check whether the request requires unsafe or expensive effects: weight download, full inference, output writes, GUI display, webcam/video capture, or checkout modification. Ask before doing those effects.
3. Use the nearest bundled helper in dry-run/smoke mode:
   • scripts/check_environment.py for root dependency/import checks.
   • sub-skills/model-and-config/scripts/inspect_darknet_config.py for cfg/names/static model compatibility.
   • sub-skills/image-detection/scripts/check_image_pipeline.py for no-weight preprocessing/postprocessing checks.
   • sub-skills/image-detection/scripts/run_image_detection.py for a validated dry-run launcher around still-image detection.
   • sub-skills/video-camera-demos/scripts/check_video_demo_args.py for parser/source checks.
   • sub-skills/video-camera-demos/scripts/run_video_demo.py for a validated dry-run launcher around video/camera demos.
4. Only execute full detector/demo commands after the user supplies local inputs and approves side effects.

Key repository facts

• Core modules: darknet, util, preprocess, and bbox.
• Main still-image entrypoint: detect.py; the generated skill wraps it through sub-skills/image-detection/scripts/run_image_detection.py.
• Video/camera entrypoints: video_demo.py, video_demo_half.py, and cam_demo.py; the generated skill wraps/preflights them through the video-camera sub-skill scripts.
• Supported constructible cfg in the inspected baseline: cfg/yolov3.cfg. Other bundled cfgs parse but contain unsupported YOLOv2-style region and/or reorg blocks.
• Default COCO names file has 80 classes; VOC names file has 20 classes and does not match unmodified cfg/yolov3.cfg.
• Input resolution must be greater than 32 and divisible by 32.

Do not use this skill when

• The user is asking for Ultralytics/YOLOv5/YOLOv8, MMDetection, Detectron2, or another modern detector framework.
• The task is training, fine-tuning, evaluation benchmarking, ONNX/TensorRT export, deployment serving, or dataset labeling unless the user explicitly ties it to this repository and accepts that the baseline skill does not cover it.
• The user only needs generic OpenCV image/video handling without this repository's YOLOv3 scripts, cfgs, weights, or failure modes.