参考：
一、数据集
下载地址：链接：
链接：https://pan.baidu.com/s/1xBph3IBXKnArVtMSckLeMA 提取码：1111
包含3523张训练图片和882张测试图片，标签格式为txt文件，每张图片对应一个txt文件。
标签格式
二、模型数据
2.1. 数据集目录配置
在data文件夹下创建head.yaml，此文件设置类别数量，类别名称以及数据集的路径。内容如下图所示：
head.yaml
YOLOv5 🚀 by Ultralytics, GPL-3.0 license
Example usage: python train.py --data head.yaml
parent
├── yolov5
└── data
└── head_datas
Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/Objects365 # dataset root dir
train: images/train # train images (relative to 'path') 1742289 images
val: images/val # val images (relative to 'path') 80000 images
test: # test images (optional)
train: ./data/head_datas/images/train/ # 16551
val: ./data/head_datas/images/val/ # 4952
Classes
nc: 365 # number of classes
nc: 1
names: ['Head']
2.2. 预训练模型
从 yolov5 github 源码地址下载预训练 yolov5s.pt、yolov5m.pt、yolov5l.pt、yolov5x.pt放置在 weights目录下，训练前需要修改models目录下yolov5.yaml文件的类别数量，使用哪个模型权重就修改对应的yaml文件。
weights
三. 训练模型
参加：
python train.py
可以按照自己的路径修改如下图所示的信息，另外epochs、batch-size也可以根据自己的显卡配置进行修改。
train.py
def parse_opt(known=False):
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default=ROOT / 'weights/yolov5s.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default=ROOT / 'data/head.yaml', help='dataset.yaml path')
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=300)
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
(yolov5) robot@robot-All-Series:~/yolov5$ python train.py
train: weights=weights/yolov5s.pt, cfg=models/yolov5s.yaml, data=data/head.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=300, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5 🚀 2022-2-27 torch 1.10.2 CUDA:0 (NVIDIA TITAN Xp, 12191MiB)
hyperparameters: 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=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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
Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)
TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=1
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 270 layers, 7022326 parameters, 7022326 gradients, 15.8 GFLOPs
Transferred 342/349 items from weights/yolov5s.pt
Scaled weight_decay = 0.0005
optimizer: SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias
WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.
See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.
train: Scanning '/home/robot/yolov5/data/head_datas/labels/train' images and labels...3523 found, 0 missing, 2 empty, 0 corrupt: 100%|█| 3523/3523 [00:00<00:0 0 1 2 3 6 28 45 54 115 161 221 299 300 640 882 6888 22415 train: new cache created: home robot yolov5 data head_datas labels train.cache val: scanning ' val' images and labels...882 found, missing, empty, corrupt: 100%|█| [00:00<00:00, val.cache plotting to runs train exp4 labels.jpg... autoanchor: 4.17 anchors target, 1.000 best possible recall (bpr). current are a good fit dataset ✅ image sizes train, val using dataloader workers logging results starting training for epochs... epoch gpu_mem box obj cls img_size 2.15g 0.0961 0.1032 640: 100%|██████████| [00:47<00:00, 4.64it s] class p r map@.5 map@.5:.95: [00:05<00:00, 5.12it all 0.57 0.516 0.532 0.147 3.23g 0.0693 0.09994 [00:43<00:00, 5.13it 5.57it 0.733 0.718 0.754 0.267 0.0649 0.1023 [00:42<00:00, 5.20it [00:04<00:00, 5.70it 0.371 0.343 0.246 0.0488 0.0571 0.09583 5.23it 5.56it 0.889 0.782 0.852 0.376 < code></00:0>
device那里，因为我是两块卡，所以编号为0,1
训练三个小时，完成。
Epoch gpu_mem box obj cls labels img_size
287/299 3.23G 0.03353 0.07164 0 98 640: 100%|██████████| 221/221 [00:43<00:00, 0 28 100 213 288 882 22415 7012822 5.06it s] class images labels p r map@.5 map@.5:.95: 100%|██████████| [00:04<00:00, 5.81it all 0.941 0.908 0.942 0.494 stopping training early as no improvement observed in last epochs. best results at epoch 187, model saved best.pt. to update earlystopping(patience="100)" pass a new patience value, i.e. python train.py --patience 300 or use --patience 0 disable earlystopping. epochs completed 3.938 hours. optimizer stripped from runs train exp4 weights last.pt, 14.5mb best.pt, validating best.pt... fusing layers... summary: layers, parameters, gradients, 15.8 gflops [00:11<00:00, 2.53it 0.94 0.944 0.498 < code></00:00,>
训练好的模型会被保存在weights/last.pt和best.pt
四、模型测试
python detect.py
python detect.py –source=data/images/test0.png –weights=weights/last.pt
模型训练得到的权重保存在runs目录下，会有对应的bese.pt及last.pt，在detect.py文件中修改模型权重路径和输入图片的路径即可。
五、 训练过程的可视化
作者是利用tensorboard来可视化训练过程的，训练开始会在主目录生成一个runs文件.利用tensorboard打开即可
tensorboard --logdir=./runs
(yolov5) robot@robot-All-Series:~/yolov5$ tensorboard --logdir=./runs
TensorFlow installation not found - running with reduced feature set.
NOTE: Using experimental fast data loading logic. To disable, pass
"--load_fast=false" and report issues on GitHub. More details:
https://github.com/tensorflow/tensorboard/issues/4784
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.8.0 at http://localhost:6006/ (Press CTRL+C to quit)
Original: https://blog.csdn.net/light169/article/details/123378140
Author: light169
Title: 深度学习之YOLOv5实践应用（3-1）人头检测模型
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