基于Yolov8网络进行目标检测（三）-训练自己的数据集

python与大数据分析

发布于 2023-09-18 15:04:11

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发布于 2023-09-18 15:04:11

文章被收录于专栏：python与大数据分析

前一篇文章详细了讲解了如何构造自己的数据集，以及如何修改模型配置文件和数据集配置文件，本篇主要是如何训练自己的数据集，并且如何验证。

VOC2012数据集下载地址：

http://host.robots.ox.ac.uk/pascal/VOC/voc2012/

coco全量数据集下载地址：

http://images.cocodtaset.org/annotations/annotations_trainval2017.zip

本篇以以下图片为预测对象。

一、对coco128数据集进行训练，coco128.yaml中已包括下载脚本，选择yolov8n轻量模型，开始训练

yolo detect train data=coco128.yaml model=model\yolov8n.pt epochs=100 imgsz=640

训练的相关截图，第一部分是展开后的命令行执行参数和网络结构

第二部分是每轮训练过程

第三部分是对各类标签的验证情况

二、对VOC2012数据集进行训练，使用我们定义的两个yaml配置文件，选择yolov8n轻量模型，开始训练

yolo detect train data=E:\JetBrains\PycharmProject\Yolov8Project\venv\Lib\site-packages\ultralytics\cfg\datasets\VOC2012.yaml model=E:\JetBrains\PycharmProject\Yolov8Project\venv\Lib\site-packages\ultralytics\cfg\models\v8\VOC2012.yaml  pretrained=model\yolov8n.pt epochs=10 imgsz=640

以下为运行日志，和上述一样

(venv) PS E:\JetBrains\PycharmProject\Yolov8Project> yolo detect train data=E:\JetBrains\PycharmProject\Yolov8Project\venv\Lib\site-packages\ultralytics\cfg\datasets\VOC2012.yaml model=E:\JetBrains\PycharmProject\Yolov8Project\venv\
 
Lib\site-packages\ultralytics\cfg\models\v8\VOC2012.yaml  pretrained=model\yolov8n.pt epochs=10 imgsz=640
 
WARNING  no model scale passed. Assuming scale='n'.
 

 
                   from  n    params  module                                       arguments                     
 
0-11464  ultralytics.nn.modules.conv.Conv[3, 16, 3, 2]                 
 
1-114672  ultralytics.nn.modules.conv.Conv[16, 32, 3, 2]                
 
2-117360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
 
3-1118560  ultralytics.nn.modules.conv.Conv[32, 64, 3, 2]                
 
4-1249664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
 
5-1173984  ultralytics.nn.modules.conv.Conv[64, 128, 3, 2]               
 
6-12197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
 
7-11295424  ultralytics.nn.modules.conv.Conv[128, 256, 3, 2]              
 
8-11460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]
 
9-11164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]
 
10-110  torch.nn.modules.upsampling.Upsample[None, 2, 'nearest']
 
11[-1, 6]  10  ultralytics.nn.modules.conv.Concat[1]
 
12-11148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]
 
13-110  torch.nn.modules.upsampling.Upsample[None, 2, 'nearest']
 
14[-1, 4]  10  ultralytics.nn.modules.conv.Concat[1]
 
15-1137248  ultralytics.nn.modules.block.C2f             [192, 64, 1]
 
16-1136992  ultralytics.nn.modules.conv.Conv[64, 64, 3, 2]
 
17[-1, 12]  10  ultralytics.nn.modules.conv.Concat[1]
 
18-11123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]
 
19-11147712  ultralytics.nn.modules.conv.Conv[128, 128, 3, 2]              
 
20[-1, 9]  10  ultralytics.nn.modules.conv.Concat[1]
 
21-11493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]
 
22[15, 18, 21]  1755212  ultralytics.nn.modules.head.Detect[20, [64, 128, 256]]
 
VOC2012 summary: 225 layers, 3014748 parameters, 3014732 gradients
 

 
Transferred319/355 items from pretrained weights
 
UltralyticsYOLOv8.0.178Python-3.10.11 torch-2.0.1+cu118 CUDA:0(Quadro P2200, 5120MiB)
 
engine\trainer: task=detect, mode=train, model=E:\JetBrains\PycharmProject\Yolov8Project\venv\Lib\site-packages\ultralytics\cfg\models\v8\VOC2012.yaml, data=E:\JetBrains\PycharmProject\Yolov8Project\venv\Lib\site-packages\ultralytic
 
s\cfg\datasets\VOC2012.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=model\yolov8n.pt, optimizer=auto, verbose=
 
True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, 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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, stream_bu
 
ffer=False, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, w
 
orkspace=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, hs
 
v_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\train8
 
WARNING  no model scale passed. Assuming scale='n'.
 

 
                   from  n    params  module                                       arguments
 
0-11464  ultralytics.nn.modules.conv.Conv[3, 16, 3, 2]
 
1-114672  ultralytics.nn.modules.conv.Conv[16, 32, 3, 2]
 
2-117360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
 
train: Scanning E:\JetBrains\PyCharm Project\ObjectDetectionProject\datasets\VOC2012\labels\train.cache... 17125 images, 195 backgrounds, 0 corrupt: 100%|██████████| 17125/17125[00:00<?, ?it/s]
 
val: Scanning E:\JetBrains\PyCharm Project\ObjectDetectionProject\datasets\VOC2012\labels\train.cache... 17125 images, 195 backgrounds, 0 corrupt: 100%|██████████| 17125/17125[00:00<?, ?it/s]
 
Plotting labels to runs\detect\train8\labels.jpg...
 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
 
optimizer: AdamW(lr=0.000417, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
 
Image sizes 640 train, 640 val
 
Using8 dataloader workers
 
Logging results to runs\detect\train8
 
Starting training for10 epochs...
 
Closing dataloader mosaic
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
1/102.41G0.91562.5721.24410640: 100%|██████████| 1071/1071[07:06<00:00,  2.51it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:44<00:00,  3.26it/s]
 
                   all      17125349130.6210.5720.6050.436
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
2/102.53G1.0061.8691.31110640: 100%|██████████| 1071/1071[07:06<00:00,  2.51it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:40<00:00,  3.35it/s]
 
                   all      17125349130.6440.540.5920.414
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
3/102.49G1.0381.6611.3449640: 100%|██████████| 1071/1071[07:02<00:00,  2.54it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:44<00:00,  3.25it/s]
 
                   all      17125349130.6160.5620.5940.419
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
4/102.47G1.0211.4931.33112640: 100%|██████████| 1071/1071[07:00<00:00,  2.55it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:42<00:00,  3.29it/s]
 
                   all      17125349130.6510.5880.6380.457
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
5/102.48G1.0051.4031.3184640: 100%|██████████| 1071/1071[07:00<00:00,  2.54it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:41<00:00,  3.31it/s]
 
                   all      17125349130.6730.5920.650.467
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
6/102.46G0.96821.2991.299640: 100%|██████████| 1071/1071[06:55<00:00,  2.58it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:29<00:00,  3.58it/s]
 
                   all      17125349130.7090.6230.6930.511
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
7/102.48G0.9321.2091.2618640: 100%|██████████| 1071/1071[06:57<00:00,  2.56it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:39<00:00,  3.37it/s]
 
                   all      17125349130.7210.6610.7220.542
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
8/102.49G0.89611.1271.2329640: 100%|██████████| 1071/1071[07:00<00:00,  2.55it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:40<00:00,  3.35it/s]
 
                   all      17125349130.7350.670.7460.567
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
9/102.47G0.85651.0581.2028640: 100%|██████████| 1071/1071[06:58<00:00,  2.56it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:29<00:00,  3.59it/s]
 
                   all      17125349130.7660.6960.7730.597
 

 
Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  InstancesSize
 
10/102.45G0.82780.98891.17911640: 100%|██████████| 1071/1071[06:55<00:00,  2.58it/s]
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:28<00:00,  3.61it/s]
 
                   all      17125349130.7770.7180.7950.621
 

 
10 epochs completed in 1.620 hours.
 
Optimizer stripped from runs\detect\train8\weights\last.pt, 6.2MB
 
Optimizer stripped from runs\detect\train8\weights\best.pt, 6.2MB
 

 
Validating runs\detect\train8\weights\best.pt...
 
UltralyticsYOLOv8.0.178Python-3.10.11 torch-2.0.1+cu118 CUDA:0(Quadro P2200, 5120MiB)
 
VOC2012 summary (fused): 168 layers, 3009548 parameters, 0 gradients
 
ClassImagesInstancesBox(P          R      mAP50  mAP50-95): 100%|██████████| 536/536[02:31<00:00,  3.54it/s]
 
                   all      17125349130.7770.7180.7950.621
 
             aeroplane      171259110.9240.8130.9020.731
 
               bicycle      171257530.7650.5780.7370.582
 
                  bird      1712511690.8940.7570.8620.651
 
                  boat      171259020.7560.6410.7260.506
 
                bottle      1712513290.7230.5940.6790.489
 
                   bus      171256380.8930.8180.8940.775
 
                   car      1712521050.7860.690.7990.618
 
                   cat      1712512660.8520.880.9210.763
 
                 chair      1712524430.7060.5610.660.482
 
                   cow      171256420.7820.8040.8580.673
 
           diningtable      171256350.5910.7180.690.517
 
                   dog      1712515710.8460.7950.8830.727
 
                 horse      171257600.6730.6340.740.61
 
                person      17125157530.790.8390.8750.691
 
           pottedplant      1712510550.7010.5250.6140.404
 
                 sheep      171258780.7750.8230.8580.665
 
                  sofa      171255920.7030.6440.730.592
 
                 train      171256720.8820.8440.9140.735
 
             tvmonitor      171258390.730.6770.7650.595
 
Speed: 0.2ms preprocess, 3.9ms inference, 0.0ms loss, 0.7ms postprocess per image
 
Results saved to runs\detect\train8
 
Learn more at https://docs.ultralytics.com/modes/train
 
(venv) PS E:\JetBrains\PycharmProject\Yolov8Project>

三、将run\detect\trainx\best.pt拷贝到model目录下，并改为相关可辨识的模型名称

四、执行测试代码，验证一下几个训练模型的预测结果

from ultralytics import YOLO
 
from PIL importImage
 
filepath='test\eat.png'
 

 
# 直接加载预训练模型
 
model = YOLO('model\yolov8x.pt')
 
# Run inference on 'bus.jpg'
 
results = model(filepath)  # results list
 
# Show the results
 
for r in results:
 
    im_array = r.plot()  # plot a BGR numpy array of predictions
 
    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
 
    im.show()  # show image
 
    im.save('yolov8x.jpg')  # save image
 

 
# 直接加载预训练模型
 
model = YOLO('model\yolov8n.pt')
 
# Run inference on 'bus.jpg'
 
results = model(filepath)  # results list
 
# Show the results
 
for r in results:
 
    im_array = r.plot()  # plot a BGR numpy array of predictions
 
    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
 
    im.show()  # show image
 
    im.save('yolov8n.jpg')  # save image
 

 
# 直接加载预训练模型
 
model = YOLO('model\coco128.pt')
 
# Run inference on 'bus.jpg'
 
results = model(filepath)  # results list
 
# Show the results
 
for r in results:
 
    im_array = r.plot()  # plot a BGR numpy array of predictions
 
    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
 
    im.show()  # show image
 
    im.save('coco128.jpg')  # save image
 

 
# 直接加载预训练模型
 
model = YOLO('model\VOC2012.pt')
 
# Run inference on 'bus.jpg'
 
results = model(filepath)  # results list
 
# Show the results
 
for r in results:
 
    im_array = r.plot()  # plot a BGR numpy array of predictions
 
    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
 
    im.show()  # show image
 
    im.save('VOC2012.jpg')  # save image

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原始发表：2023-09-16 00:00，如有侵权请联系 cloudcommunity@tencent.com 删除