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name: classification description: "Use this YOLOv5 sub-skill for image-classification training, validation, prediction, ImageFolder data, YOLOv5-cls checkpoints, and torchvision model choices." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

YOLOv5 Classification

Use this route for YOLOv5 image classification workflows involving classify/predict.py, classify/train.py, classify/val.py, yolov5*-cls.pt checkpoints, torchvision classifier model names, ImageFolder datasets, or classification-specific output/troubleshooting.

Choose the workflow

• Predict classes: read references/workflows.md for source handling, image size, device, save options, and classifier output expectations.
• Train classifiers: use the training section for named datasets, local ImageFolder directories, pretrained models, freeze/dropout/cutoff choices, and DDP.
• Validate classifiers: use the validation section for accuracy checks, ImageNet-style layout, batch size, half/DNN, and output directories.
• Data layout: read references/data-formats.md before using a custom directory or named dataset.

Use scripts/plan_classification_command.py to preview commands without downloads, training, media processing, or output writes.

Common decisions

• Use yolov5s-cls.pt or a small torchvision model for smoke-scale planning.
• Use local ImageFolder paths for reproducible work; named datasets may download.
• Classification uses probabilities/top-k outputs, not boxes or masks.
• Keep --project, --name, and --exist-ok explicit.
• Use CPU for parser/import checks and CUDA for realistic training.

Handoffs

• Route detection boxes to ../detection/SKILL.md and masks to ../segmentation/SKILL.md.
• Route checkpoint export to ../export/SKILL.md after selecting a classifier checkpoint.
• Read root references/datasets-and-weights.md for checkpoint names and ImageNet download risks.