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【计算机视觉】YOLO 入门：训练 COCO128 数据集

作者：AllinToyou | 2024-02-27 03:22:04

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coco128

一、COCO128 数据集

我们以最近大热的YOLOv8为例，回顾一下之前的安装过程：

%pip install ultralytics
import ultralytics
ultralytics.checks()

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在这里插入图片描述
这里选择训练的数据集为：COCO128

COCO128是一个小型教程数据集，由COCOtrain2017中的前128个图像组成。

在YOLO中自带的coco128.yaml文件：

1）可选的用于自动下载的下载命令/URL，

2）指向培训图像目录的路径（或指向带有培训图像列表的*.txt文件的路径），

3）与验证图像相同，

4）类数，

5）类名列表：

# download command/URL (optional)
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip

# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ../coco128/images/train2017/
val: ../coco128/images/train2017/

# number of classes
nc: 80

# class names
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 
        'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 
        'teddy bear', 'hair drier', 'toothbrush']

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二、训练过程

!yolo train model = yolov8n.pt data = coco128.yaml epochs = 10 imgsz = 640

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训练过程为：

                   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      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, Tr
ue]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    897664  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]          
Model summary: 225 layers, 3157200 parameters, 3157184 gradients

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Transferred 355/355 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /kaggle/working/datasets/coco128/labels/train2017.cache... 126 i
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/datasets/coco128/labels/train2017.cache... 126 ima
Plotting labels to runs/detect/train/labels.jpg... 
optimizer: AdamW(lr=0.000119, 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
Using 2 dataloader workers
Logging results to runs/detect/train
Starting training for 10 epochs...
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))

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      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      2.61G      1.153      1.398      1.192         81        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.688      0.506       0.61      0.446

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      2.56G      1.142      1.345      1.202        121        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.678      0.525       0.63      0.456

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      2.57G      1.147       1.25      1.175        108        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.656      0.548       0.64      0.466

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      2.57G      1.149      1.287      1.177        116        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.684      0.568      0.654      0.482

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      2.57G      1.169      1.233      1.207         68        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.664      0.586      0.668      0.491

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      2.57G      1.139      1.231      1.177         95        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929       0.66      0.613      0.677        0.5

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      2.57G      1.134      1.211      1.181        115        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.649      0.631      0.683      0.504

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      2.57G      1.114      1.194      1.178         71        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.664      0.634       0.69      0.513

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      2.57G      1.117      1.127      1.148        142        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.624      0.671      0.697       0.52

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      2.57G      1.085      1.133      1.172        104        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.631      0.676      0.704      0.522

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10 epochs completed in 0.018 hours.
Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB
Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB

Validating runs/detect/train/weights/best.pt...
Ultralytics YOLOv8.0.128 
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