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name: training-and-configuration description: "Choose, validate, and launch YOLOv7-d2 Detectron2 configs, custom COCO datasets, augmentations, optimizers, and training/evaluation workflows." metadata: disco-role: operating disable-model-invocation: true license: GPL 3.0

Training and Configuration

Use this sub-skill when the user needs to choose a YOLOv7-d2 config, register a custom dataset, inspect model family settings, build a training/evaluation command, tune augmentations, or debug Detectron2 config/data errors.

Start here

1. Identify the model family from MODEL.META_ARCHITECTURE or the user's config name. Read references/config-and-models.md.
2. If the user has custom data, validate COCO JSON/image roots before training. Read references/custom-datasets.md and run scripts/validate_coco_detection_json.py.
3. Build the command with scripts/build_train_command.py, then review references/training-workflows.md.
4. For Python Detectron2 LazyConfig files, read references/lazyconfig.md.
5. For exact API/default facts, read references/api-reference.md.
6. If anything fails, use references/troubleshooting.md.

Route by workflow

• Standard YOLO-family detection (YOLO, YOLOV5, YOLOV6, YOLOV7, YOLOV7P, YOLOX, YOLOF): use the standard detection trainer pattern.
• SparseInst or mask-only instance segmentation: use the instance-segmentation trainer pattern and mask-aware evaluation notes.
• DETR-family (Detr, AnchorDetr, SMCADetr, DetrD2go): use the transformer trainer pattern and DETR optimizer/mapper notes.
• Python LazyConfig files: use the LazyConfig launcher pattern, not the broken LazyConfig demo.
• Anchor recalculation for custom anchor-based data: use scripts/compute_anchors_from_coco.py on a valid COCO annotation file.

Safe checks

Run these before long jobs:

python scripts/inspect_yolov7_config.py --config path/to/config.yaml
python scripts/validate_coco_detection_json.py --json annotations.json --images image_root
python scripts/build_train_command.py --mode det --config path/to/config.yaml --num-gpus 1 --opts MODEL.WEIGHTS path/to/model.pth

These helpers do not train. They help catch missing configs, bad dataset schemas, unresolved _BASE_ paths, wrong class counts, or unsafe command choices before launching expensive runs.

Boundaries

• For PyTorch demo inference or COCO evaluation command details, read ../inference-and-evaluation/SKILL.md.
• For ONNX export, ONNXRuntime, TensorRT, quantization, or DETR checkpoint conversion, read ../deployment-and-export/SKILL.md.