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 ultralyticsimport ultralyticsultralytics.checks() 这里选择训练的数据集为：
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()
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 gradientsTransferred 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 0.52210 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：
来源地址：https://blog.csdn.net/wzk4869/article/details/132557041
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本文标题: 【计算机视觉】YOLO 入门：训练 COCO128 数据集
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