省赛中药检测模型调优
目录
- 一、baseline性能
- 二、baseline+ DETR head
- 三、baseline+ RepC3K2
- 四、baseline+ RepC3K2 + SimSPPF
- 五、baseline+ RepC3K2 + SimSPPF + LK-C2PSA
- 六、baseline+ RepC3K2 + SimSPPF + ~~LK-C2PSA~~ TriAttentionPSA
- 界面
- 最后一步：量化，格式导出，稳定性测试
- 后勤：
- 总结
一、baseline性能
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size120/120 6.91G 1.374 1.145 1.657 3 832: 100%|██████████| 482/482 [00:20<00:00, 23.48it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:01<00:00, 19.14it/s]all 1005 2152 0.782 0.739 0.811 0.474120 epochs completed in 0.792 hours.
Optimizer stripped from runs/ChineseMedTrain/exp8/weights/last.pt, 5.5MB
Optimizer stripped from runs/ChineseMedTrain/exp8/weights/best.pt, 5.5MBValidating runs/ChineseMedTrain/exp8/weights/best.pt...
Ultralytics 8.3.7 🚀 Python-3.9.19 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 4090, 24209MiB)
YOLO11n summary (fused): 238 layers, 2,591,902 parameters, 0 gradients, 6.4 GFLOPsClass Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:02<00:00, 15.02it/s]all 1005 2152 0.788 0.735 0.811 0.474ginseng 34 57 0.869 0.772 0.864 0.483Leech 20 41 0.778 0.769 0.847 0.513JujubaeFructus 18 67 0.829 0.761 0.86 0.491LiliiBulbus 18 19 0.64 0.789 0.835 0.552CoptidisRhizoma 22 22 0.868 0.898 0.96 0.758MumeFructus 21 98 0.716 0.693 0.756 0.372MagnoliaBark 21 45 0.737 0.746 0.814 0.416Oyster 18 24 0.735 0.809 0.846 0.595Seahorse 14 33 0.835 0.424 0.493 0.274Luohanguo 17 21 0.834 0.714 0.793 0.593GlycyrrhizaUralensis 18 25 0.92 0.92 0.978 0.502Sanqi 32 42 0.753 0.714 0.761 0.544TetrapanacisMedulla 19 20 0.859 0.915 0.977 0.622CoicisSemen 24 35 0.88 0.628 0.823 0.492LyciiFructus 20 32 0.829 0.562 0.772 0.411TruestarAnise 18 60 0.853 0.679 0.894 0.376ClamShell 17 67 0.699 0.746 0.765 0.466Chuanxiong 28 69 0.782 0.623 0.766 0.372Garlic 24 70 0.801 0.748 0.793 0.341GinkgoBiloba 27 119 0.767 0.807 0.859 0.532ChrysanthemiFlos 13 20 0.786 0.7 0.734 0.436
AtractylodesMacrocephala 15 23 0.807 0.909 0.886 0.576JuglandisSemen 12 45 0.87 0.448 0.689 0.332TallGastrodiae 17 35 0.577 0.74 0.689 0.339TrionycisCarapax 15 22 0.666 0.636 0.749 0.515AngelicaRoot 18 35 0.78 0.886 0.89 0.538Hawthorn 21 47 0.683 0.366 0.565 0.253CrociStigma 20 22 0.951 0.874 0.948 0.523SerpentisPeriostracum 16 16 0.864 0.875 0.929 0.598EucommiaBark 17 32 0.844 0.781 0.841 0.484ImperataeRhizoma 21 22 0.904 0.909 0.944 0.579LoniceraJaponica 12 25 0.525 0.531 0.549 0.279Zhizi 20 128 0.806 0.336 0.589 0.242Scorpion 13 21 0.812 0.81 0.867 0.619HouttuyniaeHerba 16 16 0.952 1 0.995 0.596EupolyphagaSinensis 19 48 0.641 0.875 0.856 0.509OroxylumIndicum 31 67 0.827 0.821 0.886 0.458CurcumaLonga 34 63 0.718 0.726 0.738 0.444NelumbinisPlumula 17 20 0.797 0.7 0.748 0.458ArecaeSemen 22 66 0.668 0.424 0.71 0.352Scolopendra 19 25 0.801 0.6 0.667 0.437MoriFructus 22 64 0.725 0.688 0.687 0.3
FritillariaeCirrhosaeBulbus 24 26 0.747 0.846 0.87 0.561DioscoreaeRhizoma 23 34 0.896 0.757 0.911 0.45CicadaePeriostracum 17 41 0.824 0.927 0.914 0.531PiperCubeba 21 28 0.825 0.821 0.873 0.504BupleuriRadix 22 25 0.814 0.72 0.889 0.499AntelopeHom 18 48 0.771 0.839 0.853 0.556Pangdahai 19 71 0.859 0.769 0.882 0.575NelumbinisSemen 19 51 0.674 0.73 0.764 0.447
Speed: 0.2ms preprocess, 0.3ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/ChineseMedTrain/exp8
二、baseline+ DETR head
提醒：在yolo11之后添加RT-DETR会失败；正确的思路是利用RT-DETR作为baseline
经过测试，采用RT-DETR检测头，导致训练速度降低4倍。
三、baseline+ RepC3K2
改进的点：C3K2重参数化 Rep技术；
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size120/120 13.9G 1.225 0.874 1.51 45 352: 100%|██████████| 241/241 [00:18<00:00, 12.76it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 16/16 [00:01<00:00, 9.69it/s]all 1005 2152 0.833 0.792 0.854 0.527120 epochs completed in 0.716 hours.
Optimizer stripped from runs/ChineseMedTrain/exp2/weights/last.pt, 5.6MB
Optimizer stripped from runs/ChineseMedTrain/exp2/weights/best.pt, 5.6MBValidating runs/ChineseMedTrain/exp2/weights/best.pt...
WARNING ⚠️ validating an untrained model YAML will result in 0 mAP.
Ultralytics 8.3.7 🚀 Python-3.9.19 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 4090, 24209MiB)
YOLO11RepC3K2 summary (fused): 239 layers, 2,591,902 parameters, 0 gradients, 6.4 GFLOPsClass Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 16/16 [00:02<00:00, 6.57it/s]all 1005 2152 0.833 0.79 0.854 0.527ginseng 34 57 0.859 0.853 0.914 0.581Leech 20 41 0.759 0.845 0.884 0.621JujubaeFructus 18 67 0.891 0.856 0.911 0.545LiliiBulbus 18 19 0.82 0.895 0.898 0.563CoptidisRhizoma 22 22 0.868 0.895 0.97 0.799MumeFructus 21 98 0.747 0.694 0.784 0.408MagnoliaBark 21 45 0.889 0.844 0.906 0.521Oyster 18 24 0.756 0.917 0.929 0.685Seahorse 14 33 0.942 0.489 0.593 0.345Luohanguo 17 21 0.767 0.762 0.777 0.618GlycyrrhizaUralensis 18 25 0.886 1 0.989 0.505Sanqi 32 42 0.735 0.714 0.788 0.586TetrapanacisMedulla 19 20 0.959 0.95 0.993 0.626CoicisSemen 24 35 0.936 0.835 0.922 0.59LyciiFructus 20 32 0.832 0.562 0.715 0.428TruestarAnise 18 60 0.936 0.732 0.939 0.415ClamShell 17 67 0.806 0.672 0.801 0.493Chuanxiong 28 69 0.797 0.783 0.803 0.439Garlic 24 70 0.806 0.643 0.817 0.421GinkgoBiloba 27 119 0.86 0.823 0.899 0.572ChrysanthemiFlos 13 20 0.788 0.75 0.71 0.429
AtractylodesMacrocephala 15 23 0.858 0.826 0.902 0.603JuglandisSemen 12 45 0.906 0.639 0.83 0.395TallGastrodiae 17 35 0.726 0.758 0.799 0.436TrionycisCarapax 15 22 0.782 0.818 0.811 0.601AngelicaRoot 18 35 0.877 0.914 0.909 0.586Hawthorn 21 47 0.873 0.638 0.754 0.374CrociStigma 20 22 1 0.952 0.957 0.541SerpentisPeriostracum 16 16 0.853 0.938 0.966 0.736EucommiaBark 17 32 0.854 0.875 0.913 0.572ImperataeRhizoma 21 22 0.913 0.955 0.95 0.621LoniceraJaponica 12 25 0.547 0.6 0.695 0.331Zhizi 20 128 0.82 0.5 0.685 0.309Scorpion 13 21 0.833 0.857 0.873 0.606HouttuyniaeHerba 16 16 0.951 1 0.995 0.65EupolyphagaSinensis 19 48 0.76 0.958 0.88 0.561OroxylumIndicum 31 67 0.838 0.821 0.932 0.51CurcumaLonga 34 63 0.667 0.698 0.815 0.501NelumbinisPlumula 17 20 0.886 0.7 0.777 0.51ArecaeSemen 22 66 0.879 0.667 0.894 0.455Scolopendra 19 25 0.776 0.64 0.638 0.467MoriFructus 22 64 0.719 0.679 0.677 0.307
FritillariaeCirrhosaeBulbus 24 26 0.736 0.846 0.906 0.641DioscoreaeRhizoma 23 34 0.828 0.847 0.909 0.493CicadaePeriostracum 17 41 0.828 0.937 0.914 0.581PiperCubeba 21 28 0.92 0.821 0.869 0.576BupleuriRadix 22 25 0.938 0.8 0.924 0.507AntelopeHom 18 48 0.817 0.812 0.911 0.584Pangdahai 19 71 0.869 0.746 0.874 0.583NelumbinisSemen 19 51 0.763 0.759 0.803 0.512
Speed: 0.4ms preprocess, 0.4ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/ChineseMedTrain/exp2
四、baseline+ RepC3K2 + SimSPPF
改进的点：SimSPPF简化SPPF模块；
engine/trainer: task=detect, mode=train, model=yolo11RepC3K2SimSPPF.yaml, data=ultralytics/cfg/datasets/originalChineseMed50.yaml, epochs=120, time=None, patience=150, batch=32, imgsz=640, save=True, save_period=10, cache=False, device=0, workers=8, project=runs/ChineseMedTrain, name=exp3, exist_ok=False, pretrained=/home/wqt/Projects/yolov11/ultralytics/runs/ChineseMedTrain/exp2/weights/best.pt, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=True, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=True, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.2, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/ChineseMedTrain/exp3
Overriding model.yaml nc=80 with nc=50
WARNING ⚠️ no model scale passed. Assuming scale='n'.from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 6640 ultralytics.nn.modules.block.RepC3k2 [32, 64, 1, False, 0.25] 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 4 -1 1 26080 ultralytics.nn.modules.block.RepC3k2 [64, 128, 1, False, 0.25] 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 6 -1 1 89216 ultralytics.nn.modules.block.RepC3k2 [128, 128, 1, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 354560 ultralytics.nn.modules.block.RepC3k2 [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SimSPPF [256, 256, 5]
10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 111296 ultralytics.nn.modules.block.RepC3k2 [384, 128, 1, False]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 32096 ultralytics.nn.modules.block.RepC3k2 [256, 64, 1, False]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 86720 ultralytics.nn.modules.block.RepC3k2 [192, 128, 1, False]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 387328 ultralytics.nn.modules.block.RepC3k2 [384, 256, 1, True]
23 [16, 19, 22] 1 440422 ultralytics.nn.modules.head.Detect [50, [64, 128, 256]]
YOLO11RepC3K2SimSPPF summary: 360 layers, 2,618,662 parameters, 2,618,646 gradients, 6.5 GFLOPs
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size120/120 13.8G 1.149 0.7983 1.44 45 352: 100%|██████████| 241/241 [00:19<00:00, 12.35it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 16/16 [00:01<00:00, 9.26it/s]all 1005 2152 0.867 0.799 0.872 0.549120 epochs completed in 0.714 hours.
Optimizer stripped from runs/ChineseMedTrain/exp3/weights/last.pt, 5.6MB
Optimizer stripped from runs/ChineseMedTrain/exp3/weights/best.pt, 5.6MBValidating runs/ChineseMedTrain/exp3/weights/best.pt...
WARNING ⚠️ validating an untrained model YAML will result in 0 mAP.
Ultralytics 8.3.7 🚀 Python-3.9.19 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 4090, 24209MiB)
YOLO11RepC3K2SimSPPF summary (fused): 245 layers, 2,592,286 parameters, 0 gradients, 6.4 GFLOPsClass Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 16/16 [00:02<00:00, 6.30it/s]all 1005 2152 0.867 0.802 0.871 0.549ginseng 34 57 0.888 0.833 0.907 0.587Leech 20 41 0.815 0.902 0.932 0.65JujubaeFructus 18 67 0.904 0.839 0.968 0.585LiliiBulbus 18 19 0.946 0.895 0.9 0.588CoptidisRhizoma 22 22 0.874 0.955 0.984 0.824MumeFructus 21 98 0.746 0.781 0.802 0.411MagnoliaBark 21 45 0.848 0.865 0.932 0.576Oyster 18 24 0.841 1 0.971 0.706Seahorse 14 33 0.835 0.613 0.627 0.37Luohanguo 17 21 0.876 0.667 0.79 0.613GlycyrrhizaUralensis 18 25 0.926 0.995 0.984 0.537Sanqi 32 42 0.908 0.643 0.818 0.625TetrapanacisMedulla 19 20 0.984 0.95 0.987 0.672CoicisSemen 24 35 0.823 0.829 0.907 0.6LyciiFructus 20 32 0.865 0.599 0.758 0.459TruestarAnise 18 60 1 0.678 0.953 0.432ClamShell 17 67 0.806 0.731 0.841 0.538Chuanxiong 28 69 0.775 0.768 0.796 0.427Garlic 24 70 0.848 0.715 0.858 0.435GinkgoBiloba 27 119 0.847 0.866 0.914 0.61ChrysanthemiFlos 13 20 0.937 0.745 0.788 0.488
AtractylodesMacrocephala 15 23 0.826 0.828 0.893 0.614JuglandisSemen 12 45 0.912 0.694 0.785 0.389TallGastrodiae 17 35 0.75 0.743 0.764 0.408TrionycisCarapax 15 22 0.866 0.818 0.865 0.61AngelicaRoot 18 35 0.894 0.914 0.904 0.582Hawthorn 21 47 0.979 0.66 0.766 0.434CrociStigma 20 22 0.91 0.864 0.934 0.529SerpentisPeriostracum 16 16 0.974 0.938 0.988 0.727EucommiaBark 17 32 0.942 0.812 0.942 0.629ImperataeRhizoma 21 22 0.879 0.909 0.974 0.653LoniceraJaponica 12 25 0.779 0.565 0.707 0.383Zhizi 20 128 0.923 0.563 0.765 0.355Scorpion 13 21 0.845 0.905 0.935 0.679HouttuyniaeHerba 16 16 0.846 0.938 0.986 0.662EupolyphagaSinensis 19 48 0.757 0.976 0.924 0.62OroxylumIndicum 31 67 0.82 0.885 0.872 0.474CurcumaLonga 34 63 0.836 0.73 0.857 0.521NelumbinisPlumula 17 20 0.708 0.7 0.778 0.514ArecaeSemen 22 66 0.942 0.745 0.923 0.471Scolopendra 19 25 0.878 0.64 0.669 0.471MoriFructus 22 64 0.77 0.719 0.758 0.355
FritillariaeCirrhosaeBulbus 24 26 0.841 0.885 0.936 0.65DioscoreaeRhizoma 23 34 0.846 0.811 0.919 0.528CicadaePeriostracum 17 41 0.865 0.951 0.931 0.636PiperCubeba 21 28 0.865 0.857 0.91 0.579BupleuriRadix 22 25 1 0.739 0.907 0.557AntelopeHom 18 48 0.929 0.771 0.899 0.623Pangdahai 19 71 0.872 0.861 0.897 0.603NelumbinisSemen 19 51 0.787 0.804 0.761 0.473
Speed: 0.3ms preprocess, 0.4ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/ChineseMedTrain/exp3
五、baseline+ RepC3K2 + SimSPPF + LK-C2PSA
改进的点：将PSA模块中的Attention修改为Deformable-LK Attention，即可变形的大核Attention；
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size120/120 12.8G 1.122 0.7926 1.414 45 352: 100%|██████████| 241/241 [00:41<00:00, 5.74it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 16/16 [00:03<00:00, 4.89it/s]all 1005 2152 0.865 0.805 0.867 0.556120 epochs completed in 1.539 hours.
Optimizer stripped from runs/ChineseMedTrain/exp4/weights/last.pt, 7.0MB
Optimizer stripped from runs/ChineseMedTrain/exp4/weights/best.pt, 7.0MBValidating runs/ChineseMedTrain/exp4/weights/best.pt...
WARNING ⚠️ validating an untrained model YAML will result in 0 mAP.
Ultralytics 8.3.7 🚀 Python-3.9.19 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 4090, 24209MiB)
YOLO11RepC3K2SimSPPF_LKPSA summary (fused): 243 layers, 3,342,258 parameters, 0 gradients, 7.0 GFLOPsClass Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 16/16 [00:04<00:00, 3.94it/s]all 1005 2152 0.859 0.806 0.867 0.557ginseng 34 57 0.915 0.76 0.912 0.592Leech 20 41 0.825 0.927 0.926 0.637JujubaeFructus 18 67 0.875 0.91 0.932 0.572LiliiBulbus 18 19 0.917 0.842 0.906 0.61CoptidisRhizoma 22 22 0.912 1 0.992 0.839MumeFructus 21 98 0.79 0.816 0.826 0.46MagnoliaBark 21 45 0.866 0.933 0.961 0.587Oyster 18 24 0.74 0.958 0.947 0.713Seahorse 14 33 0.928 0.485 0.612 0.376Luohanguo 17 21 0.785 0.667 0.816 0.611GlycyrrhizaUralensis 18 25 0.924 0.98 0.973 0.547Sanqi 32 42 0.946 0.714 0.867 0.669TetrapanacisMedulla 19 20 1 0.968 0.995 0.684CoicisSemen 24 35 0.881 0.914 0.951 0.604LyciiFructus 20 32 0.835 0.625 0.783 0.463TruestarAnise 18 60 0.873 0.783 0.897 0.406ClamShell 17 67 0.758 0.642 0.745 0.489Chuanxiong 28 69 0.795 0.754 0.796 0.439Garlic 24 70 0.931 0.767 0.89 0.472GinkgoBiloba 27 119 0.814 0.866 0.917 0.584ChrysanthemiFlos 13 20 0.833 0.8 0.798 0.495
AtractylodesMacrocephala 15 23 0.742 0.826 0.813 0.595JuglandisSemen 12 45 0.899 0.592 0.767 0.391TallGastrodiae 17 35 0.843 0.686 0.818 0.45TrionycisCarapax 15 22 0.856 0.818 0.877 0.682AngelicaRoot 18 35 0.893 0.914 0.898 0.622Hawthorn 21 47 0.904 0.681 0.781 0.466CrociStigma 20 22 0.817 0.909 0.94 0.529SerpentisPeriostracum 16 16 0.886 0.973 0.97 0.751EucommiaBark 17 32 0.854 0.906 0.92 0.597ImperataeRhizoma 21 22 0.936 0.955 0.954 0.662LoniceraJaponica 12 25 0.604 0.64 0.642 0.315Zhizi 20 128 0.879 0.509 0.711 0.339Scorpion 13 21 0.861 0.905 0.967 0.706HouttuyniaeHerba 16 16 0.975 0.938 0.972 0.706EupolyphagaSinensis 19 48 0.76 1 0.947 0.614OroxylumIndicum 31 67 0.861 0.776 0.822 0.484CurcumaLonga 34 63 0.793 0.762 0.856 0.533NelumbinisPlumula 17 20 0.801 0.7 0.727 0.517ArecaeSemen 22 66 0.937 0.667 0.901 0.475Scolopendra 19 25 0.913 0.64 0.641 0.492MoriFructus 22 64 0.832 0.695 0.786 0.388
FritillariaeCirrhosaeBulbus 24 26 0.898 0.885 0.932 0.658DioscoreaeRhizoma 23 34 0.904 0.827 0.968 0.522CicadaePeriostracum 17 41 0.865 0.938 0.946 0.66PiperCubeba 21 28 0.76 0.791 0.905 0.583BupleuriRadix 22 25 0.955 0.854 0.929 0.576AntelopeHom 18 48 0.979 0.812 0.935 0.663Pangdahai 19 71 0.791 0.789 0.844 0.569NelumbinisSemen 19 51 0.787 0.796 0.734 0.435
Speed: 0.3ms preprocess, 2.0ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/ChineseMedTrain/exp4
该改进的亮点是，mAP50-95的性能得到提升，说明它在复杂场景下的识别率有所提升。
六、baseline+ RepC3K2 + SimSPPF + LK-C2PSA TriAttentionPSA
改进的点：将PSA模块中的Attention修改为 采用以下博客中的技术，即TriAttention，三重注意力机制来改进PSA模块；
https://blog.csdn.net/m0_63774211/article/details/145569867
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 6640 ultralytics.nn.modules.block.RepC3k2 [32, 64, 1, False, 0.25] 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 4 -1 1 26080 ultralytics.nn.modules.block.RepC3k2 [64, 128, 1, False, 0.25] 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 6 -1 1 89216 ultralytics.nn.modules.block.RepC3k2 [128, 128, 1, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 354560 ultralytics.nn.modules.block.RepC3k2 [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SimSPPF [256, 256, 5] 10 -1 1 198700 ultralytics.nn.modules.block.TriC2PSA [256, 256, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 111296 ultralytics.nn.modules.block.RepC3k2 [384, 128, 1, False] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 1 32096 ultralytics.nn.modules.block.RepC3k2 [256, 64, 1, False] 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 86720 ultralytics.nn.modules.block.RepC3k2 [192, 128, 1, False] 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 1 387328 ultralytics.nn.modules.block.RepC3k2 [384, 256, 1, True] 23 [16, 19, 22] 1 440422 ultralytics.nn.modules.head.Detect [50, [64, 128, 256]]
YOLO11RepC3K2SimSPPF_TriPSA summary: 363 layers, 2,567,634 parameters, 2,567,618 gradients, 6.5 GFLOPsTransferred 484/535 items from pretrained weights
以下是运行的结果：
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size120/120 13.5G 1.127 0.8044 1.416 45 352: 100%|██████████| 241/241 [00:18<00:00, 12.77it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 16/16 [00:01<00:00, 9.72it/s]all 1005 2152 0.87 0.8 0.865 0.556120 epochs completed in 0.713 hours.
Optimizer stripped from runs/ChineseMedTrain/exp5/weights/last.pt, 5.5MB
Optimizer stripped from runs/ChineseMedTrain/exp5/weights/best.pt, 5.5MBValidating runs/ChineseMedTrain/exp5/weights/best.pt...
WARNING ⚠️ validating an untrained model YAML will result in 0 mAP.
Ultralytics 8.3.7 🚀 Python-3.9.19 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 4090, 24209MiB)
YOLO11RepC3K2SimSPPF_TriPSA summary (fused): 251 layers, 2,541,770 parameters, 0 gradients, 6.3 GFLOPsClass Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 16/16 [00:02<00:00, 6.52it/s]all 1005 2152 0.87 0.797 0.864 0.557ginseng 34 57 0.894 0.739 0.911 0.616Leech 20 41 0.881 0.902 0.931 0.66JujubaeFructus 18 67 0.903 0.834 0.921 0.579LiliiBulbus 18 19 0.889 0.841 0.873 0.578CoptidisRhizoma 22 22 0.911 0.926 0.983 0.822MumeFructus 21 98 0.76 0.786 0.817 0.427MagnoliaBark 21 45 0.809 0.844 0.92 0.572Oyster 18 24 0.83 0.917 0.942 0.697Seahorse 14 33 0.941 0.486 0.601 0.387Luohanguo 17 21 0.89 0.768 0.851 0.636GlycyrrhizaUralensis 18 25 0.926 0.998 0.978 0.594Sanqi 32 42 0.902 0.643 0.82 0.652TetrapanacisMedulla 19 20 0.981 0.95 0.99 0.678CoicisSemen 24 35 0.884 0.857 0.924 0.617LyciiFructus 20 32 0.868 0.617 0.785 0.47TruestarAnise 18 60 0.961 0.827 0.94 0.445ClamShell 17 67 0.783 0.701 0.765 0.495Chuanxiong 28 69 0.832 0.754 0.803 0.434Garlic 24 70 0.824 0.686 0.816 0.453GinkgoBiloba 27 119 0.841 0.888 0.922 0.608ChrysanthemiFlos 13 20 0.84 0.789 0.782 0.511
AtractylodesMacrocephala 15 23 0.782 0.826 0.843 0.583JuglandisSemen 12 45 0.922 0.6 0.711 0.406TallGastrodiae 17 35 0.787 0.629 0.759 0.409TrionycisCarapax 15 22 0.82 0.831 0.865 0.652AngelicaRoot 18 35 0.894 0.914 0.902 0.606Hawthorn 21 47 0.896 0.681 0.761 0.458CrociStigma 20 22 0.899 0.909 0.931 0.529
SerpentisPeriostracum 16 16 0.938 0.939 0.988 0.754EucommiaBark 17 32 0.939 0.938 0.948 0.619ImperataeRhizoma 21 22 0.942 0.955 0.989 0.654LoniceraJaponica 12 25 0.751 0.64 0.72 0.379Zhizi 20 128 0.827 0.522 0.709 0.336Scorpion 13 21 0.883 0.952 0.955 0.678HouttuyniaeHerba 16 16 0.982 0.938 0.981 0.662EupolyphagaSinensis 19 48 0.716 0.979 0.917 0.635OroxylumIndicum 31 67 0.872 0.761 0.82 0.487CurcumaLonga 34 63 0.801 0.683 0.856 0.553NelumbinisPlumula 17 20 0.794 0.7 0.74 0.504ArecaeSemen 22 66 0.953 0.697 0.926 0.494Scolopendra 19 25 0.898 0.6 0.685 0.518MoriFructus 22 64 0.861 0.675 0.8 0.382
FritillariaeCirrhosaeBulbus 24 26 0.836 0.885 0.902 0.641DioscoreaeRhizoma 23 34 0.842 0.824 0.936 0.55CicadaePeriostracum 17 41 0.857 0.951 0.893 0.6PiperCubeba 21 28 0.886 0.832 0.908 0.582BupleuriRadix 22 25 0.97 0.88 0.92 0.555AntelopeHom 18 48 0.997 0.833 0.93 0.659Pangdahai 19 71 0.812 0.789 0.844 0.574NelumbinisSemen 19 51 0.807 0.74 0.77 0.457
Speed: 0.4ms preprocess, 0.3ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/ChineseMedTrain/exp5
界面
需要搭建一个QT界面；用于展示系统；
最后一步：量化，格式导出，稳定性测试
需要将最后迭代的模型，进行量化；
再转为能在CPU下跑 onnx格式；
最后，将code+model放到测试机器上，作为现场的测试设备，100%保证演示的稳定性；
后勤：
大家提前做好出行安排，保留好收据和发票等，后续报销会用到；
总结
基于以上改进，我们优化了一个更快更好的模型，来实现中草药的检测；
最后，祝每一个参赛的同学能够取得好成绩，你们的积极参与，终将有所回报！