map is lower than 38.3

[yuedongli1]

I trained yolov9-t with coco, my command is this:
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_dual.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-t.yaml --weights '' --name yolov9-t --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
but the map of the best.pt is 37.4

[yuedongli1]

val command is:
python val_dual.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './runs/train/yolov9-t5/weights/best.pt' --save-json --name yolov9_t_640_val

[LarryXing0]

I trained yolov9-t with coco, my command is this: python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_dual.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-t.yaml --weights '' --name yolov9-t --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15 but the map of the best.pt is 37.4

after training. metric is flowing,do you solve the probelm

Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.291
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.421
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.311
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.314
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.282
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.468
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.506
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.281
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.556
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.681
@yuedongli1