<div align="center">
<p>
<a align="left" href="https://ultralytics.com/yolov5" target="_blank">
<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
</p>
<br>
<div>
<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
<a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
</div>
<br>
<div align="center">
<a href="https://github.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.linkedin.com/company/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://twitter.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://youtube.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.facebook.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.instagram.com/ultralytics/">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
</a>
</div>
<br>
<p>
YOLOv5 ð is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
</p>
<!--
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
-->
</div>
## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
## <div align="center">Quick Start Examples</div>
<details open>
<summary>Install</summary>
[**Python>=3.6.0**](https://www.python.org/) is required with all
[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
```bash
$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt
```
</details>
<details open>
<summary>Inference</summary>
Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
```python
import torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom
# Images
img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
# Inference
results = model(img)
# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```
</details>
<details>
<summary>Inference with detect.py</summary>
`detect.py` runs inference on a variety of sources, downloading models automatically from
the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
```bash
$ python detect.py --source 0 # webcam
file.jpg # image
file.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/NUsoVlDFqZg' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
</details>
<details>
<summary>Training</summary>
Run commands below to reproduce results
on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on
first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the
largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
```bash
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 40
yolov5l 24
yolov5x 16
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
</details>
<details open>
<summary>Tutorials</summary>
* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) ð RECOMMENDED
* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) âï¸
RECOMMENDED
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) ð NEW
* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) ð NEW
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) â NEW
* [TorchScript, ONNX, CoreML Export](https://github.com/ultralytics/yolov5/issues/251) ð
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) â NEW
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
</details>
## <div align="center">Environments</div>
Get started in seconds with our verified environments. Click each icon below for details.
<div align="center">
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
</a>
<a href="https://www.kaggle.com/ultralytics/yolov5">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
</a>
<a href="https://hub.docker.com/r/ultralytics/yolov5">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
</a>
<a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
</a>
<a href="https:/
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PCB板缺陷检测系统源代码,基于yolov5-6.0自己的数据集+基于python的检测和C++的部署检测.zip
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PCB板缺陷检测系统源代码,基于yolov5-6.0自己的数据集+基于python的检测和C++的部署检测.zip相关工作 (1)解压数据集,放在yolov5-6.0根目录下 (2)删除ImageSets文件夹下的内容,在/ImageSets文件夹下新建Main文件夹 (3)数据集分类,运行test.py(注意路径正确)。会在/ImageSets/Main文件夹下生成四个txt文件,trainval.txt、test.txt、train.txt、val.txt。 (4)在data文件夹下新建labels文件夹,然后在yolov5-6.0文件夹下新建my_labels.py文件,并运行(注意路径,注意数据集类别)。会在data文件夹下生成三个txt文件,train.txt、val.txt、test.txt。 (5)在models文件夹下新建文件my_pcb.yaml(注意修改类别数量nc) (6)在data文件夹下新建my_data.yaml(注意train和val路径、nc和数据类别名称names) (7)准备预训练文件yolov5s.pt,放在weights文件夹下。 (8)开始训练
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PCB板缺陷检测系统源代码,基于yolov5-6.0自己的数据集+基于python的检测和C++的部署检测.zip (1096个子文件)
events.out.tfevents.1652762577.lyp.28315.0 1.24MB
CMakeDetermineCompilerABI_CXX.bin 8KB
CMakeDetermineCompilerABI_C.bin 8KB
CMakeCCompilerId.c 20KB
train.cache 126KB
val.cache 14KB
cmake.check_cache 85B
Makefile.cmake 8KB
CMakeCXXCompiler.cmake 5KB
CMakeCCompiler.cmake 2KB
cmake_install.cmake 2KB
DependInfo.cmake 2KB
DependInfo.cmake 2KB
CMakeDirectoryInformation.cmake 684B
CMakeSystem.cmake 402B
cmake_clean.cmake 239B
cmake_clean.cmake 230B
CMakeCXXCompilerId.cpp 20KB
yolo.cpp 5KB
main.cpp 1KB
results.csv 29KB
Dockerfile 2KB
Dockerfile 821B
.dockerignore 4KB
yolo.h 2KB
CXX.includecache 32KB
CXX.includecache 32KB
depend.internal 8KB
depend.internal 8KB
tutorial.ipynb 48KB
04_missing_hole_01.jpg 1.63MB
01_short_02.jpg 1.44MB
05_mouse_bite_08.jpg 1.27MB
05_spur_07.jpg 1.27MB
05_spur_07.jpg 1.17MB
val_batch1_pred.jpg 623KB
val_batch1_labels.jpg 613KB
val_batch2_pred.jpg 551KB
val_batch2_labels.jpg 531KB
train_batch2.jpg 505KB
val_batch0_pred.jpg 502KB
train_batch0.jpg 499KB
val_batch0_labels.jpg 494KB
bus.jpg 483KB
bus.jpg 483KB
bus.jpg 483KB
bus.jpg 476KB
train_batch1.jpg 452KB
labels_correlogram.jpg 366KB
labels.jpg 305KB
zidane.jpg 247KB
zidane.jpg 247KB
zidane.jpg 247KB
zidane.jpg 165KB
LICENSE 34KB
CMakeOutput.log 44KB
main 65KB
depend.make 11KB
depend.make 11KB
build.make 5KB
build.make 5KB
flags.make 1KB
flags.make 1KB
progress.make 43B
progress.make 43B
Makefile 6KB
Makefile2 5KB
progress.marks 2B
README.md 14KB
my_readme.md 13KB
README.md 10KB
CONTRIBUTING.md 5KB
README.md 2KB
yolo.cpp.o 101KB
main.cpp.o 89KB
yolov5s.onnx 27.98MB
best.onnx 27.21MB
a.out 8KB
a.out 8KB
1103.png 1.44MB
results.png 234KB
F1_curve.png 234KB
R_curve.png 205KB
P_curve.png 154KB
confusion_matrix.png 139KB
PR_curve.png 127KB
yolov5s.pt 14.12MB
best.pt 13.81MB
datasets.py 43KB
general.py 33KB
train.py 31KB
wandb_utils.py 25KB
tf.py 20KB
common.py 20KB
plots.py 19KB
val.py 17KB
export.py 16KB
detect.py 15KB
yolo.py 14KB
torch_utils.py 14KB
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