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name: yolov3 description: "Operate Ultralytics YOLOv3 detection workflows: inference, training, validation, architecture, export, and maintenance." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Ultralytics YOLOv3 Repo Skill

Use this skill for the Ultralytics YOLOv3 repository: detection-only YOLOv3, YOLOv3-SPP, and YOLOv3-tiny workflows.

Route by task

• Inference, detect.py, PyTorch Hub, DetectMultiBackend, labels, crops, or input sources: read sub-skills/inference/SKILL.md.
• Training, custom dataset YAMLs, hyperparameters, checkpoints, resume, DDP, or smoke training: read sub-skills/training/SKILL.md.
• Validation, mAP, val.py, COCO JSON, speed/study, or saved validation artifacts: read sub-skills/validation-evaluation/SKILL.md.
• Model YAMLs, anchors, Detect, Model, parse_model, or output-shape debugging: read sub-skills/model-architecture/SKILL.md.
• Export formats, deployment suffixes, export.py, ONNX, OpenVINO, TensorRT, CoreML, TorchScript, or Paddle: read sub-skills/export-deployment/SKILL.md.
• Repository changes, CI smokes, dependency floors, docs, PR policy, or packaging metadata: read sub-skills/repo-maintenance/SKILL.md.

Public setup

YOLOv3 is normally used from a source checkout:

python -m pip install -r requirements.txt
python - <<'PY'
import torch
from models.yolo import Model
m = Model('models/yolov3-tiny.yaml').eval()
y = m(torch.zeros(1, 3, 64, 64))[0]
print(tuple(y.shape))
PY

Expected tiny-model shape for 80 classes is (1, 60, 85). Official weights such as yolov3-tiny.pt may download on first use, so ask before running network-dependent native commands.

Shared references

• references/repo-provenance.md records the source snapshot and evidence basis.
• references/package-and-environment.md summarizes install, dependency, and backend facts.
• references/troubleshooting.md covers cross-cutting failures.
• scripts/yolov3_repo_smoke_plan.py prints safe native smoke-test commands and safety notes without running them.