YOLOv8训练自己的数据集(足球检测)

YOLOv8训练自己的数据集(足球检测)

    • 前言
    • 前提条件
    • 实验环境
    • 安装环境
    • 项目地址
      • Linux
      • Windows
    • 制作自己的数据集
    • 训练自己的数据集
      • 创建自己数据集的yaml文件
        • football.yaml文件内容
      • 进行训练
      • 进行验证
      • 进行预测
    • 数据集获取
    • 参考文献

前言

  • 本文是个人使用YOLOv8训练自己的YOLO格式数据集的应用案例,由于水平有限,难免出现错漏,敬请批评改正。
  • 虽然YOLOv8与YOLOv5都是同一个团队Ultralytics发布的,但是YOLOv8的代码封装性比YOLOv5更好。
  • YOLOv8要求的数据集格式与YOLOv5、YOLOv7一致。
  • YOLOv8最大的改变就是抛弃了以往的anchor-base,使用了anchor-free的思想
  • 更多精彩内容,可点击进入YOLO系列专栏或我的个人主页查看

前提条件

  • 熟悉Python

实验环境

matplotlib>=3.2.2
numpy>=1.18.5
opencv-python>=4.6.0
Pillow>=7.1.2
PyYAML>=5.3.1
requests>=2.23.0
scipy>=1.4.1
torch>=1.7.0
torchvision>=0.8.1
tqdm>=4.64.0
tensorboard>=2.4.1
pandas>=1.1.4
seaborn>=0.11.0

安装环境

pip install ultralytics

项目地址

官方YOLOv8源代码地址:https://github.com/ultralytics/ultralytics.git
本文章项目地址:https://gitcode.net/FriendshipTang/yolov8.git
注:本文之所以不直接克隆官方YOLOv8源代码地址,是因为:

  • 我在源代码基础上,下载好并添加了yolov8s.pt权重文件和新建并编辑好了关于足球数据集信息的football.yaml文件,便于后续使用。
  • 如果直接克隆官方YOLOv8源代码地址,你会发现会出现一个这样的路径"/ultralytics/ultralytics",这可能会导致from ultralytics import YOLOimport ultralytics报错。

Linux

git clone https://gitcode.net/FriendshipTang/yolov8.git
Cloning into 'yolov8'...
remote: Enumerating objects: 4583, done.
remote: Counting objects: 100% (4583/4583), done.
remote: Compressing objects: 100% (1270/1270), done.
remote: Total 4583 (delta 2981), reused 4576 (delta 2979), pack-reused 0
Receiving objects: 100% (4583/4583), 23.95 MiB | 1.55 MiB/s, done.
Resolving deltas: 100% (2981/2981), done.

Windows

请到https://gitcode.net/FriendshipTang/yolov8.git网站下载源代码zip压缩包。

制作自己的数据集

详见YOLOv7训练自己的数据集(口罩检测)
地址:https://blog.csdn.net/FriendshipTang/article/details/126513426

训练自己的数据集

创建自己数据集的yaml文件

football.yaml文件内容为例,大家可以根据自己的数据集信息进行修改。

football.yaml文件内容

# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ./yolov8/football_yolodataset/trainset
val: ./yolov8/football_yolodataset/testset

# number of classes
nc: 1

# class names
names: ["football"]

进行训练

yolo detect train data=football.yaml model=yolov8s.pt epochs=20 imgsz=640 device=0,1 batch=128
Ultralytics YOLOv8.0.37  Python-3.7.12 torch-1.11.0 CUDA:0 (Tesla T4, 15110MiB)
                                                      CUDA:1 (Tesla T4, 15110MiB)
yolo/engine/trainer: task=detect, mode=train, model=yolov8s.pt, data=football.yaml, epochs=20, patience=50, batch=128, imgsz=640, save=True, save_period=-1, cache=False, device=(0, 1), workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, min_memory=False, overlap_mask=True, mask_ratio=4, dropout=False, val=True, split=val, save_json=False, save_hybrid=False, conf=0.001, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=ultralytics/assets/, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, 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, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.001, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, 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=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, save_dir=runs/detect/train
Overriding model.yaml nc=80 with nc=1

                   from  n    params  module                                       arguments                     
  0                  -1  1       928  ultralytics.nn.modules.Conv                  [3, 32, 3, 2]                 
  1                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                
  2                  -1  1     29056  ultralytics.nn.modules.C2f                   [64, 64, 1, True]             
  3                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               
  4                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           
  5                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              
  6                  -1  2    788480  ultralytics.nn.modules.C2f                   [256, 256, 2, True]           
  7                  -1  1   1180672  ultralytics.nn.modules.Conv                  [256, 512, 3, 2]              
  8                  -1  1   1838080  ultralytics.nn.modules.C2f                   [512, 512, 1, True]           
  9                  -1  1    656896  ultralytics.nn.modules.SPPF                  [512, 512, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           
 12                  -1  1    591360  ultralytics.nn.modules.C2f                   [768, 256, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           
 15                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 
 16                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              
 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           
 18                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 
 19                  -1  1    590336  ultralytics.nn.modules.Conv                  [256, 256, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           
 21                  -1  1   1969152  ultralytics.nn.modules.C2f                   [768, 512, 1]                 
 22        [15, 18, 21]  1   2116435  ultralytics.nn.modules.Detect                [1, [128, 256, 512]]          
Model summary: 225 layers, 11135987 parameters, 11135971 gradients, 28.6 GFLOPs

Transferred 349/355 items from pretrained weights
DDP settings: RANK 0, WORLD_SIZE 2, DEVICE cuda:0
Overriding model.yaml nc=80 with nc=1

                   from  n    params  module                                       arguments                     
  0                  -1  1       928  ultralytics.nn.modules.Conv                  [3, 32, 3, 2]                 
  1                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                
  2                  -1  1     29056  ultralytics.nn.modules.C2f                   [64, 64, 1, True]             
  3                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               
  4                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           
  5                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              
  6                  -1  2    788480  ultralytics.nn.modules.C2f                   [256, 256, 2, True]           
  7                  -1  1   1180672  ultralytics.nn.modules.Conv                  [256, 512, 3, 2]              
  8                  -1  1   1838080  ultralytics.nn.modules.C2f                   [512, 512, 1, True]           
  9                  -1  1    656896  ultralytics.nn.modules.SPPF                  [512, 512, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           
 12                  -1  1    591360  ultralytics.nn.modules.C2f                   [768, 256, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           
 15                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 
 16                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              
 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           
 18                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 
 19                  -1  1    590336  ultralytics.nn.modules.Conv                  [256, 256, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           
 21                  -1  1   1969152  ultralytics.nn.modules.C2f                   [768, 512, 1]                 
 22        [15, 18, 21]  1   2116435  ultralytics.nn.modules.Detect                [1, [128, 256, 512]]          
Model summary: 225 layers, 11135987 parameters, 11135971 gradients, 28.6 GFLOPs

Transferred 349/355 items from pretrained weights
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.002), 63 bias
train: Scanning /kaggle/working/yolov8/football_yolodataset/trainset/labels.cach
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
val: Scanning /kaggle/working/yolov8/football_yolodataset/testset/labels.cache..
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/detect/train
Starting training for 20 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/20      13.7G      1.241      7.611      1.058         50        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.728       0.46      0.496      0.282

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/20      13.7G      1.061      1.076      0.979         46        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.791      0.497      0.566      0.338

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/20      13.7G      1.043     0.8968     0.9592         56        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.722      0.506      0.581      0.344

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/20      13.7G      1.082     0.8714     0.9542         57        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.804      0.503      0.589      0.311

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/20      13.7G      1.134      0.891     0.9604         44        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.652      0.469      0.485      0.236

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/20      13.7G      1.134     0.8498     0.9638         44        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.701      0.489      0.509      0.242

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/20      13.7G      1.152     0.8197     0.9519         49        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.759      0.485      0.549      0.254

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/20      13.7G      1.111     0.7813     0.9402         36        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.705      0.499      0.551      0.307

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/20      13.7G      1.106     0.7623     0.9441         44        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.722      0.521      0.569      0.308

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/20      13.7G      1.073     0.7442     0.9144         50        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.725      0.512       0.57      0.314
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      11/20      13.7G      1.083     0.7146     0.9452         24        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.646      0.465      0.497      0.286

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      12/20      13.7G      1.083     0.7343     0.9433         27        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.771      0.486      0.567       0.32

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      13/20      13.7G      1.071     0.6758     0.9452         26        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693       0.76      0.504      0.585      0.339

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      14/20      13.7G       1.04     0.6566     0.9366         26        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.747      0.545        0.6      0.343

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      15/20      13.7G      1.047     0.6338     0.9396         25        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.782      0.569      0.661      0.369

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      16/20      13.7G      1.008     0.6253     0.9315         26        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.777      0.605      0.669       0.39

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      17/20      13.7G     0.9794     0.5733     0.9216         27        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.747      0.606       0.67      0.394

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      18/20      13.7G     0.9408     0.5384     0.9087         25        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.798      0.619      0.708      0.416

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      19/20      13.7G     0.9406     0.5241     0.8998         26        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.841      0.647      0.719       0.43

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      20/20      13.7G     0.8838     0.5055     0.8898         29        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.864      0.659      0.756      0.454

20 epochs completed in 0.980 hours.
Optimizer stripped from runs/detect/train/weights/last.pt, 22.5MB
Optimizer stripped from runs/detect/train/weights/best.pt, 22.5MB

Validating runs/detect/train/weights/best.pt...
Model summary (fused): 168 layers, 11125971 parameters, 0 gradients, 28.4 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        683        693      0.864      0.659      0.756      0.454
Speed: 0.1ms pre-process, 3.6ms inference, 0.0ms loss, 1.0ms post-process per image
Results saved to runs/detect/train

训练完成,会在./runs/detect/train文件夹生成best.pt和last.pt权重。

进行验证

yolo detect val model=./runs/detect/train/weights/best.pt

YOLOv8训练自己的数据集(足球检测)_第1张图片

进行预测

yolo detect predict model=./runs/detect/train/weights/best.pt source="football.png"  # predict with custom model

YOLOv8训练自己的数据集(足球检测)_第2张图片

数据集获取

足球数据集

  • 地址:https://download.csdn.net/download/FriendshipTang/87354858

参考文献

[1] YOLOv8 源代码地址. https://github.com/ultralytics/ultralytics.git.
[2] YOLOv8 Docs. https://docs.ultralytics.com/

  • 更多精彩内容,可点击进入YOLO系列专栏或我的个人主页查看

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