Improving models & hyperparameter tuning

The improvement playbook (follow in order — tuning is the LAST step)

Hyperparameter tuning is expensive and usually not the bottleneck. Escalate in this order, re-validating after each step:

1. Fix the data — check confusion_matrix.png and train_batch*.jpg for label noise; review the top false-negative/false-positive val images; add examples of failing classes and true-background images. Data quality beats every other lever.
2. Train longer — if val mAP was still rising at the end: more epochs, higher patience.
3. Bigger input — small objects or mAP50 ≫ mAP50-95: raise imgsz (640 → 960/1280).
4. Bigger model — underfitting (train and val both mediocre): n → s → m → l.
5. Domain-matched augmentation — aerial degrees=180 flipud=0.5, crowded scenes copy_paste=0.3/mixup=0.1, color-critical classes lower hsv_h (see yolo-training's training-args.md).
6. Only now: hyperparameter tuning — worth ~0.5–2 mAP when everything above is exhausted.