省赛中药检测模型调优

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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；

Attention

界面

#最后一步：量化，格式导出
best.pt tensor GPU；
CPU下跑 onnx格式；

code+model放到测试机器上，作为现场的测试设备；

#后勤：
高铁：
酒店：3个房间；
180：包干；

1.引入库

代码如下（示例）：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import  ssl
ssl._create_default_https_context = ssl._create_unverified_context

2.读入数据

代码如下（示例）：

data = pd.read_csv('https://labfile.oss.aliyuncs.com/courses/1283/adult.data.csv')
print(data.head())

该处使用的url网络请求的数据。

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