YOLOv7训练数据集

1.安装环境

安装cuda和cudnn,笔者使用版本

cuda: cuda_11.6.0_511.23_windows.exe

cudnn: cudnn-windows-x86_64-8.5.0.96_cuda11-archive.zip

安装后测试

nvidia-smi 

C:\Users\#####>nvidia-smi
Fri Aug 12 18:10:49 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 511.23       Driver Version: 511.23       CUDA Version: 11.6     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ... WDDM  | 00000000:01:00.0 Off |                  N/A |
| N/A   44C    P0    31W /  N/A |      0MiB / 16384MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
 
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

nvcc -V

C:\Users\#########>nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Fri_Dec_17_18:28:54_Pacific_Standard_Time_2021
Cuda compilation tools, release 11.6, V11.6.55
Build cuda_11.6.r11.6/compiler.30794723_0
 
C:\Users\xiaodong>

安装anaconda pytorch

conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge

2.下载代码

git clone https://github.com/WongKinYiu/yolov7.git

安装环境

在yolov7路径下

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt

下载权重文件，在yolov7路径下新建文件夹weights，并复制其中

下载路径在github上，点击蓝色部分即可下载

 

 

3.制作数据集

将标注好的图片和文件放在E:\yolov7\data\VOCdevkit\VOC2007\images下

 

文件夹放置图片和标注信息

 

制作数据配置文件data/daming.yaml，参考coco.yaml

如下daming.yaml

# COCO 2017 dataset http://cocodataset.org
 
# download command/URL (optional)
 
 
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: E:\yolov7\data\VOCdevkit\VOC2007\images\train\images\
val: E:\yolov7\data\VOCdevkit\VOC2007\images\val\images\
test: E:\yolov7\data\VOCdevkit\VOC2007\images\test\images\
 
# number of classes
nc: 3
 
# class names
names: [ 'jiajing', 'hairui', 'wanli', ]

4.训练数据集

可以复制train.py修改为train_daming.py

4.1修改权重和数据路径，其他参数酌情修改

 

4.2训练命令

(torch) E:\yolov7>python train_daming.py --workers 1 --device 0 --batch-size 8 --data data/daming.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights weights/yolov7.pt --name yolov7

5.测试训练出的模型

5.1 测试图片

(torch) E:\yolov7>python detect.py --weights E:\yolov7\runs\train\yolov715\weights\best.pt  --conf 0.25 --img-size 640 --source 028.jpg

结果：E:\yolov7\runs\detect\exp19\028.jpg

5.2测试视频

(torch) E:\yolov7>python detect.py --weights E:\yolov7\runs\train\yolov715\weights\best.pt  --conf 0.25 --img-size 640 --source daming.mp4

结果：E:\yolov7\runs\detect\exp20\daming.mp4

5.3测试摄像头

(torch) E:\yolov7>python detect.py --weights E:\yolov7\runs\train\yolov715\weights\best.pt  --conf 0.25 --img-size 640 --source 0

结果：E:\yolov7\runs\detect\exp21\0.mp4