我现在是把picsize从640变化到了960，而且把原先7000张的训练数据集精简成了3600张的数据集
下面是跑出来的结果：
Validating runs/detect/yolo11-tea-yolo11s36/weights/best.pt...
Ultralytics 8.3.182 🚀 Python-3.10.12 torch-2.4.0a0+07cecf4168.nv24.05 CUDA:0 (NVIDIA A100-SXM4-80GB, 32768MiB)
YOLO11s summary (fused): 100 layers, 9,413,961 parameters, 0 gradientsClass Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:00<00:00, 6.25it/s]all 181 368 0.663 0.531 0.616 0.437algal leaf spot 69 203 0.73 0.562 0.699 0.517brown blight 77 89 0.688 0.517 0.633 0.45grey blight 66 76 0.572 0.513 0.515 0.342
Speed: 0.2ms preprocess, 1.9ms inference, 0.0ms loss, 1.3ms postprocess per image
Results saved to runs/detect/yolo11-tea-yolo11s36
Ultralytics 8.3.182 🚀 Python-3.10.12 torch-2.4.0a0+07cecf4168.nv24.05 CUDA:0 (NVIDIA A100-SXM4-80GB, 32768MiB)
YOLO11s summary (fused): 100 layers, 9,413,961 parameters, 0 gradients
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 2.9±0.9 MB/s, size: 47.8 KB)
val: Scanning /home/share/priv/yolo_new/ultralytics-main/ultralytics-main/datasets/teaDiseases/val_10_13/labels.cache... 181 images, 0 backgrounds, 0 corrupt: 100
WARNING ⚠️ cache='ram' may produce non-deterministic training results. Consider cache='disk' as a deterministic alternative if your disk space allows.
val: Caching images (0.5GB RAM): 100%|██████████| 181/181 [00:00<00:00, 193.63it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 12/12 [00:01<00:00, 6.28it/s]all 181 368 0.671 0.531 0.619 0.466algal leaf spot 69 203 0.733 0.562 0.671 0.525brown blight 77 89 0.7 0.517 0.656 0.498grey blight 66 76 0.582 0.513 0.529 0.375
Speed: 1.6ms preprocess, 3.6ms inference, 0.0ms loss, 1.4ms postprocess per image
Results saved to runs/detect/yolo11-tea-yolo11s362
验证结果： ultralytics.utils.metrics.DetMetrics object with attributes:现在尝试一下改为yolo11m.yaml+ imgsz=640,看看效果：
Validating runs/detect/yolo11-tea-yolo11s39/weights/best.pt...
Ultralytics 8.3.182 🚀 Python-3.10.12 torch-2.4.0a0+07cecf4168.nv24.05 CUDA:0 (NVIDIA A100-SXM4-80GB, 32768MiB)
YOLO11m summary (fused): 125 layers, 20,032,345 parameters, 0 gradientsClass Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:08<00:00, 2.91s/it]all 181 368 0.561 0.538 0.574 0.412algal leaf spot 69 203 0.718 0.571 0.736 0.553brown blight 77 89 0.56 0.449 0.514 0.361grey blight 66 76 0.405 0.592 0.473 0.322
Speed: 0.3ms preprocess, 4.4ms inference, 0.0ms loss, 10.2ms postprocess per image
Results saved to runs/detect/yolo11-tea-yolo11s39
Ultralytics 8.3.182 🚀 Python-3.10.12 torch-2.4.0a0+07cecf4168.nv24.05 CUDA:0 (NVIDIA A100-SXM4-80GB, 32768MiB)
YOLO11m summary (fused): 125 layers, 20,032,345 parameters, 0 gradients
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 2.6±0.8 MB/s, size: 47.8 KB)
val: Scanning /home/share/priv/yolo_new/ultralytics-main/ultralytics-main/datasets/teaDiseases/val_10_13/labels.cache... 181 images, 0 backgrounds, 0 corrupt: 100%|██████████| 181/181
WARNING ⚠️ cache='ram' may produce non-deterministic training results. Consider cache='disk' as a deterministic alternative if your disk space allows.
val: Caching images (0.2GB RAM): 100%|██████████| 181/181 [00:01<00:00, 170.08it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:08<00:00, 1.48s/it]all 181 368 0.568 0.538 0.569 0.432algal leaf spot 69 203 0.719 0.571 0.705 0.554brown blight 77 89 0.571 0.449 0.524 0.398grey blight 66 76 0.414 0.592 0.479 0.345
Speed: 1.1ms preprocess, 5.7ms inference, 0.0ms loss, 11.1ms postprocess per image
Results saved to runs/detect/yolo11-tea-yolo11s392
验证结果： ultralytics.utils.metrics.DetMetrics object with attributes:感觉m对于精度的提高没有imgsz的提高要好的多。
目前看来是yolo11s.yaml+imgsz=960时跑出的效果最好，使用的数据集是train_aug_10_14
现在尝试使用增强grey_light的图像使用960+yolo11s.yaml+train_aug_10_15进行测试，看看效果如何。
300 epochs completed in 4.279 hours.
Optimizer stripped from runs/detect/yolo11-tea-yolo11s56/weights/last.pt, 19.3MB
Optimizer stripped from runs/detect/yolo11-tea-yolo11s56/weights/best.pt, 19.3MBValidating runs/detect/yolo11-tea-yolo11s56/weights/best.pt...
Ultralytics 8.3.182 🚀 Python-3.10.12 torch-2.4.0a0+07cecf4168.nv24.05 CUDA:0 (NVIDIA A100-SXM4-80GB, 32768MiB)
YOLO11s summary (fused): 100 layers, 9,413,961 parameters, 0 gradientsClass Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:02<00:00, 2.30it/s]all 181 368 0.56 0.596 0.589 0.416algal leaf spot 69 203 0.667 0.709 0.724 0.54brown blight 77 89 0.529 0.539 0.556 0.38grey blight 66 76 0.483 0.539 0.488 0.329
Speed: 0.2ms preprocess, 4.0ms inference, 0.0ms loss, 2.4ms postprocess per image
Results saved to runs/detect/yolo11-tea-yolo11s56
Ultralytics 8.3.182 🚀 Python-3.10.12 torch-2.4.0a0+07cecf4168.nv24.05 CUDA:0 (NVIDIA A100-SXM4-80GB, 32768MiB)
YOLO11s summary (fused): 100 layers, 9,413,961 parameters, 0 gradients
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 4.3±1.2 MB/s, size: 46.9 KB)
val: Scanning /home/share/priv/yolo_new/ultralytics-main/ultralytics-main/datasets/teaDiseases/val_10_13/labels.cache... 181 images, 0 backgrounds, 0 corrupt: 100%|██████████| 181/181
WARNING ⚠️ cache='ram' may produce non-deterministic training results. Consider cache='disk' as a deterministic alternative if your disk space allows.
val: Caching images (0.5GB RAM): 100%|██████████| 181/181 [00:01<00:00, 115.39it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 12/12 [00:03<00:00, 3.93it/s]all 181 368 0.564 0.596 0.588 0.441algal leaf spot 69 203 0.668 0.709 0.7 0.553brown blight 77 89 0.54 0.539 0.56 0.409grey blight 66 76 0.484 0.539 0.503 0.36
Speed: 1.3ms preprocess, 5.1ms inference, 0.0ms loss, 2.3ms postprocess per image
Results saved to runs/detect/yolo11-tea-yolo11s562
验证结果： ultralytics.utils.metrics.DetMetrics object with attributes:结果依然没有上次的好。
现在在最好的基础上加入无参数的SimAM模块，并且更改val的验证集，查看变化。