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1# These are the exact same training arguments as used for yolov8m for fair comparison
2results = model.train(
3 data=yaml_path,
4 epochs=100, # increase for better results
5 imgsz=640,
6 batch=96, # let ultralytics decide optimal
7 device=0, # 0 = first GPU
8 project=wandb_project_name,
9 name=run_name,
10 save=True,
11 save_period=10, # checkpoint every 10 epochs
12 patience=10, # early stopping
13 workers=16, # Colab has limited CPU cores
14
15 optimizer="AdamW",
16 lr0=3.5e-3,
17 lrf=0.05, # final lr = lr0 * lrf
18 cos_lr=True,
19 warmup_epochs=3,
20 weight_decay=5e-4,
21 amp=True, # automatic mixed precision (BF16 on A100) — free speedup
22
23 close_mosaic=10,
24 mosaic=1.0,
25 mixup=0.1,
26 copy_paste=0.1, # good for occluded vehicle scenarios
27
28 # Augmentation (helps generalize to varied vehicle appearances)
29 fliplr=0.5,
30 flipud=0.0,
31 hsv_h=0.02,
32 hsv_s=0.7,
33 hsv_v=0.4,
34)