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name: yolov5 description: "Use this skill for clone-run Ultralytics YOLOv5 workflows: detection, segmentation, classification, export, benchmarks, datasets, weights, and Flask REST serving." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

YOLOv5 Repo Skill

Use this skill when a user asks about Ultralytics YOLOv5 repository workflows: object detection, instance segmentation, image classification, PyTorch Hub loading, dataset YAMLs, pretrained weights, model export, benchmarks, or the bundled Flask REST API example.

YOLOv5 is primarily a clone-run repository, not a normal import-first library. Public workflows use the repository's Python entrypoint names, shared models/ and utils/ modules, YAML configs, and checkpoint files. Prefer the bundled references and helper scripts here before reopening repository docs or examples.

Start Here

• Read references/repo-provenance.md before deciding whether this skill matches a checkout or needs refresh.
• Read references/environment.md for install, clone-run imports, Python/PyTorch requirements, CUDA, and optional extras.
• Read references/datasets-and-weights.md before planning training, validation, downloads, named datasets, or checkpoint use.
• Read references/model-overview.md when choosing between detection, segmentation, classification, P6, Hub, or exported runtime formats.
• Read references/troubleshooting.md for install/import, optional dependency, data/config, download, device, output, export, and service failures.
• Run scripts/check_yolov5_env.py --json for a safe active-environment inspection. It imports modules and checks optional dependencies without downloading models, training, exporting, opening media, or starting a server.

Route by User Goal

• Object detection: use sub-skills/detection/SKILL.md for detect.py, train.py, val.py, PyTorch Hub loading, COCO-style datasets, bounding boxes, detection checkpoints, and detection-specific failures.
• Instance segmentation: use sub-skills/segmentation/SKILL.md for segment/predict.py, segment/train.py, segment/val.py, *-seg.pt checkpoints, mask labels, mask output options, and segmentation validation.
• Image classification: use sub-skills/classification/SKILL.md for classify/predict.py, classify/train.py, classify/val.py, YOLOv5-cls or torchvision classifier models, and ImageFolder/ImageNet-style datasets.
• Export and benchmarks: use sub-skills/export/SKILL.md for export.py, benchmarks.py, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow/TFLite/TF.js, Paddle, Edge TPU, dynamic shapes, half precision, and backend prerequisite checks.
• Flask serving: use sub-skills/serving/SKILL.md for the YOLOv5 Flask REST API pattern, upload validation, API-key behavior, client requests, and safe smoke checks.

Ordered Handoffs

• New custom detection project: references/datasets-and-weights.md → sub-skills/detection/ → sub-skills/export/ only after a checkpoint exists.
• Segmentation project: references/datasets-and-weights.md → sub-skills/segmentation/ → sub-skills/export/ if deployment format conversion is needed.
• Classification project: sub-skills/classification/ → references/datasets-and-weights.md for ImageFolder/named datasets → sub-skills/export/ for deployment formats.
• REST API service: sub-skills/serving/ first; route to sub-skills/detection/ only for model behavior and to sub-skills/export/ only when the user wants non-PyTorch deployment artifacts.
• Benchmark/export request without a trained checkpoint: first choose or train the task-specific model, then use sub-skills/export/.

Common First Decisions

• Repository position: most commands assume the user is acting in a YOLOv5 checkout or has otherwise made the repository modules importable.
• Task family: detection uses boxes; segmentation uses boxes plus masks; classification uses class probabilities and ImageFolder-style labels.
• Weights: names such as yolov5s.pt, yolov5s-seg.pt, or yolov5s-cls.pt may trigger downloads. Prefer explicit local paths for offline or deterministic work.
• Datasets: training/validation commands depend on data YAMLs or ImageFolder directories. Validate dataset paths and class counts before launching expensive runs.
• Side effects: prediction, training, validation, export, benchmarks, downloads, and servers can write output directories, fetch files, open streams, or run for a long time. Prefer planner/checker scripts before execution.
• Optional dependencies: install export, logging, service, and accelerator packages narrowly for the selected workflow only.
• Hardware: CPU is enough for parser/import checks and some tiny workflows, but CUDA is strongly preferred for real training; TensorRT and some half-precision paths require matching GPU/runtime support.

Safe Baseline

python - <<'PY'
import torch
import models.common, models.yolo, utils.general
print('torch', torch.__version__)
print('cuda_available', torch.cuda.is_available())
print('yolov5 modules importable')
PY

If imports fail, read references/environment.md and references/troubleshooting.md before changing dependencies.

Bundled Helpers

• scripts/check_yolov5_env.py: safe environment, import, backend, and optional-dependency checker.
• sub-skills/detection/scripts/plan_detection_command.py: prints detection train/val/predict command previews with risk warnings.
• sub-skills/segmentation/scripts/plan_segmentation_command.py: prints segmentation train/val/predict command previews with mask/data warnings.
• sub-skills/classification/scripts/plan_classification_command.py: prints classification train/val/predict command previews with ImageFolder/model warnings.
• sub-skills/export/scripts/check_export_prereqs.py: checks optional dependencies for export formats without exporting.
• sub-skills/serving/scripts/rest_api_smoke.py: uses a Flask test client and dummy model to verify REST API request validation without downloading weights or starting a server.

Safety Policy

Do not run downloads, training, validation, prediction on streams, export conversion, benchmarks, notebooks, or long-lived servers until the task has explicit inputs, output locations, runtime budget, and backend expectations. Run original repo tests/examples only as verification after classifying them as safe for the current environment.