目录
3.1修改./yolov5/data/coco128.yaml
1.首先安装YOLOv5需要的环境
为了不和服务器上其他的环境冲突，这里使用conda创建yolov5需要的环境，
conda create -n yolov5 python=3.8
conda activate 然后下载yolov5工程
git clone https://github.com/ultralytics/yolov5 # clone repo
cd yolov5
pip install -r requirements.txt # install dependencies2.数据集准备
这里我们用labelimg标注自己的数据集，labelimg的安装见：https://blog.csdn.net/u013171226/article/details/115013514
利用labelimg标注数据的时候，注意选择生成的txt格式为yolo格式，如下图所示：
标注完成之后，我们得到的是图片数据以及和图片数据一一对应的label数据，我们把图片数据放到images文件夹里面，并且在images里面分成train2017和val2017两个文件夹，标签的txt放到labels文件夹里面，并且labels里面分成train2017和val2017两部分。
在github的yolov5工程里面有利用yolov5训练自己数据的说明：https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
这里也是模仿coco128的数据格式去准备自己的数据集，首先下载coco128数据集，
cd ./yolov5
wget https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
unar coco128.zip
然后我们把coco128里面原来的images和labels文件夹删掉，然后把我们上面标注之后得到的images文件夹和labels文件夹放到coco128里面。
3.修改相关配置文件/参数
3.1修改./yolov5/data/coco128.yaml
# COCO 2017 dataset http://cocodataset.org - first 128 training images
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to /yolov5:
# /parent_folder
# /coco128
# /yolov5
# 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: ../yolov5/coco128/images/train2017/ # 128 images
val: ../yolov5/coco128/images/train2017/ # 128 images
# number of classes
nc: 1
# class names
names: [ 'logo']1.把download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip 注释掉。
2.修改train val那里的路径。
3.修改分类树木nc
4.修改names里面的分类名称。
3.2修改yolov5s.yaml
yolov5提供了多个模型供选择，
这里我们选择YOLOv5s，那么相应的我们就要修改yolov5s.yaml，只需要修改nc数量即可，
# parameters
nc: 1 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple3.3修改train.py
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='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='data/coco128.yaml', help='data.yaml path')
parser.add_argument('--hyp', type=str, default='data/hyp.scratch.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')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model')
parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
parser.add_argument('--project', default='runs/train', help='save to project/name')
parser.add_argument('--entity', default=None, help='W&B entity')
parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--quad', action='store_true', help='quad dataloader')
parser.add_argument('--linear-lr', action='store_true', help='linear LR')
opt = parser.parse_args()由于我们选用的是yolov5s，所以修改parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', 然后执行python train.py开始训练
4.测试
修改detect.py，我这里是把检测出来的目标裁剪成子图然后保存下来，增加如下代码(从第6行到22行为增加的代码)
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
print("line---")
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
print(xywh)
print(im0.shape[1])
print(im0.shape[0])
width = im0.shape[1]
height = im0.shape[0]
x = int(xywh[0]*width)
y = int(xywh[1]*height)
w = int(xywh[2]*width)
h = int(xywh[3]*height)
x1 = int(x - w/2)
x2 = int(x + w/2)
y1 = int(y - h/2)
y2 = height
print("x1,x2,y1,y2", x1,x2,y1,y2)
imgSon = im0[y1:y2, x1:x2]
print("save_path",save_path)
cv2.imwrite(save_path, imgSon)
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or view_img: # Add bbox to image
label = f'{names[int(cls)]} {conf:.2f}'
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)