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【计算机视觉】YOLO 入门:训练 COCO128 数据集

2023-08-30 10:55

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我们以最近大热的YOLOv8为例,回顾一下之前的安装过程:

%pip install ultralyticsimport ultralyticsultralytics.checks()

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 classesnc: 80# class namesnames: ['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']
!yolo train model = yolov8n.pt data = coco128.yaml epochs = 10 imgsz = 640

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, True]               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
Transferred 355/355 items from pretrained weightsTensorBoard: 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 ialbumentations: 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 imaPlotting 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 valUsing 2 dataloader workersLogging results to runs/detect/trainStarting training for 10 epochs...Closing dataloader mosaicalbumentations: 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       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
10 epochs completed in 0.018 hours.Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MBOptimizer stripped from runs/detect/train/weights/best.pt, 6.5MBValidating runs/detect/train/weights/best.pt...Ultralytics YOLOv8.0.128 🚀 Python-3.10.10 torch-2.0.0 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)Model summary (fused): 168 layers, 3151904 parameters, 0 gradients
                 Class     Images  Instances      Box(P          R      mAP50  m                   all        128        929      0.629      0.677      0.704      0.523                person        128        254      0.763      0.721      0.778      0.569               bicycle        128          6      0.765      0.333      0.391      0.321                   car        128         46      0.487      0.217      0.322      0.192            motorcycle        128          5      0.613        0.8      0.906      0.732              airplane        128          6      0.842          1      0.972      0.809                   bus        128          7      0.832      0.714      0.712       0.61                 train        128          3       0.52          1      0.995      0.858                 truck        128         12      0.597        0.5      0.547      0.373                  boat        128          6      0.526      0.167      0.448      0.328         traffic light        128         14      0.471      0.214      0.184      0.145             stop sign        128          2      0.671          1      0.995      0.647                 bench        128          9      0.675      0.695       0.72      0.489                  bird        128         16      0.936      0.921      0.961       0.67                   cat        128          4      0.818          1      0.995      0.772                   dog        128          9       0.68      0.889      0.908      0.722                 horse        128          2      0.441          1      0.828      0.497              elephant        128         17      0.742      0.848      0.933       0.71                  bear        128          1      0.461          1      0.995      0.995                 zebra        128          4       0.85          1      0.995      0.972               giraffe        128          9      0.824          1      0.995      0.772              backpack        128          6      0.596      0.333      0.394      0.257              umbrella        128         18      0.564      0.722      0.681      0.429               handbag        128         19      0.635      0.185      0.326      0.178                   tie        128          7      0.671      0.714      0.758      0.522              suitcase        128          4      0.687          1      0.945      0.603               frisbee        128          5       0.52        0.8      0.799      0.689                  skis        128          1      0.694          1      0.995      0.497             snowboard        128          7      0.499      0.714      0.732      0.589           sports ball        128          6      0.747      0.494      0.573      0.342                  kite        128         10      0.539        0.5      0.504      0.181          baseball bat        128          4      0.595        0.5      0.509      0.253        baseball glove        128          7      0.808      0.429      0.431      0.318            skateboard        128          5      0.493        0.6      0.609      0.465         tennis racket        128          7      0.451      0.286      0.446      0.274                bottle        128         18        0.4      0.389      0.365      0.257            wine glass        128         16      0.597      0.557      0.675      0.366                   cup        128         36      0.586      0.389      0.465      0.338                  fork        128          6      0.582      0.167      0.306      0.234                 knife        128         16      0.621      0.625      0.669      0.405                 spoon        128         22      0.525      0.364       0.41      0.227                  bowl        128         28      0.657      0.714      0.719      0.584                banana        128          1      0.319          1      0.497     0.0622              sandwich        128          2      0.812          1      0.995      0.995                orange        128          4      0.784          1      0.895      0.594              broccoli        128         11      0.431      0.273      0.339       0.26                carrot        128         24      0.553      0.833      0.801      0.504               hot dog        128          2      0.474          1      0.995      0.946                 pizza        128          5      0.736          1      0.995      0.882                 donut        128         14      0.574          1      0.929       0.85                  cake        128          4      0.769          1      0.995       0.89                 chair        128         35      0.503      0.571      0.542      0.307                 couch        128          6      0.526      0.667      0.805      0.612          potted plant        128         14      0.479      0.786      0.784      0.545                   bed        128          3      0.714          1      0.995       0.83          dining table        128         13      0.451      0.615      0.552      0.437                toilet        128          2          1      0.942      0.995      0.946                    tv        128          2      0.622          1      0.995      0.846                laptop        128          3          1      0.452      0.863      0.738                 mouse        128          2          1          0     0.0459    0.00459                remote        128          8      0.736        0.5       0.62      0.527            cell phone        128          8     0.0541      0.027     0.0731      0.043             microwave        128          3      0.773      0.667      0.913      0.807                  oven        128          5      0.442      0.483      0.433      0.336                  sink        128          6      0.378      0.167      0.336      0.231          refrigerator        128          5      0.662      0.786      0.778      0.616                  book        128         29       0.47      0.336      0.402       0.23                 clock        128          9       0.76      0.778      0.884      0.762                  vase        128          2      0.428          1      0.828      0.745              scissors        128          1      0.911          1      0.995      0.256            teddy bear        128         21      0.551      0.667      0.805      0.515            toothbrush        128          5      0.768          1      0.995       0.65Speed: 3.4ms preprocess, 1.9ms inference, 0.0ms loss, 2.4ms postprocess per imageResults saved to runs/detect/train
!yolo val model = yolov8n.pt data = coco128.yaml

输出的结果为:

                 Class     Images  Instances      Box(P          R      mAP50  m                   all        128        929       0.64      0.537      0.605      0.446                person        128        254      0.797      0.677      0.764      0.538               bicycle        128          6      0.514      0.333      0.315      0.264                   car        128         46      0.813      0.217      0.273      0.168            motorcycle        128          5      0.687      0.887      0.898      0.685              airplane        128          6       0.82      0.833      0.927      0.675                   bus        128          7      0.491      0.714      0.728      0.671                 train        128          3      0.534      0.667      0.706      0.604                 truck        128         12          1      0.332      0.473      0.297                  boat        128          6      0.226      0.167      0.316      0.134         traffic light        128         14      0.734        0.2      0.202      0.139             stop sign        128          2          1      0.992      0.995      0.701                 bench        128          9      0.839      0.582       0.62      0.365                  bird        128         16      0.921      0.728      0.864       0.51                   cat        128          4      0.875          1      0.995      0.791                   dog        128          9      0.603      0.889      0.785      0.585                 horse        128          2      0.597          1      0.995      0.518              elephant        128         17      0.849      0.765        0.9      0.679                  bear        128          1      0.593          1      0.995      0.995                 zebra        128          4      0.848          1      0.995      0.965               giraffe        128          9       0.72          1      0.951      0.722              backpack        128          6      0.589      0.333      0.376      0.232              umbrella        128         18      0.804        0.5      0.643      0.414               handbag        128         19      0.424     0.0526      0.165     0.0889                   tie        128          7      0.804      0.714      0.674      0.476              suitcase        128          4      0.635      0.883      0.745      0.534               frisbee        128          5      0.675        0.8      0.759      0.688                  skis        128          1      0.567          1      0.995      0.497             snowboard        128          7      0.742      0.714      0.747        0.5           sports ball        128          6      0.716      0.433      0.485      0.278                  kite        128         10      0.817       0.45      0.569      0.184          baseball bat        128          4      0.551       0.25      0.353      0.175        baseball glove        128          7      0.624      0.429      0.429      0.293            skateboard        128          5      0.846        0.6        0.6       0.41         tennis racket        128          7      0.726      0.387      0.487       0.33                bottle        128         18      0.448      0.389      0.376      0.208            wine glass        128         16      0.743      0.362      0.584      0.333                   cup        128         36       0.58      0.278      0.404       0.29                  fork        128          6      0.527      0.167      0.246      0.184                 knife        128         16      0.564        0.5       0.59       0.36                 spoon        128         22      0.597      0.182      0.328       0.19                  bowl        128         28      0.648      0.643      0.618      0.491                banana        128          1          0          0      0.124     0.0379              sandwich        128          2      0.249        0.5      0.308      0.308                orange        128          4          1       0.31      0.995      0.623              broccoli        128         11      0.374      0.182      0.249      0.203                carrot        128         24      0.648      0.458      0.572      0.362               hot dog        128          2      0.351      0.553      0.745      0.721                 pizza        128          5      0.644          1      0.995      0.843                 donut        128         14      0.657          1       0.94      0.864                  cake        128          4      0.618          1      0.945      0.845                 chair        128         35      0.506      0.514      0.442      0.239                 couch        128          6      0.463        0.5      0.706      0.555          potted plant        128         14       0.65      0.643      0.711      0.472                   bed        128          3      0.698      0.667      0.789      0.625          dining table        128         13      0.432      0.615      0.485      0.366                toilet        128          2      0.615        0.5      0.695      0.676                    tv        128          2      0.373       0.62      0.745      0.696                laptop        128          3          1          0      0.451      0.361                 mouse        128          2          1          0     0.0625    0.00625                remote        128          8      0.843        0.5      0.605      0.529            cell phone        128          8          0          0     0.0549     0.0393             microwave        128          3      0.435      0.667      0.806      0.718                  oven        128          5      0.412        0.4      0.339       0.27                  sink        128          6       0.35      0.167      0.182      0.129          refrigerator        128          5      0.589        0.4      0.604      0.452                  book        128         29      0.629      0.103      0.346      0.178                 clock        128          9      0.788       0.83      0.875       0.74                  vase        128          2      0.376          1      0.828      0.795              scissors        128          1          1          0      0.249     0.0746            teddy bear        128         21      0.877      0.333      0.591      0.394            toothbrush        128          5      0.743        0.6      0.638      0.374Speed: 1.0ms preprocess, 8.5ms inference, 0.0ms loss, 1.6ms postprocess per imageResults saved to runs/detect/val

可视化的结果为:

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来源地址:https://blog.csdn.net/wzk4869/article/details/132557041

阅读原文内容投诉

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