Flame detection based on yolov3 training in Python version

mind_ programmonkey 2020-11-13 04:19:34
flame detection based yolov3 yolov


It's been a long time , Never blogged ( Low voice bb, I'm busy with my private work recently ). School is about to begin , harm , I'm going back to work !!!

In this tutorial, write a pytorch Version of yolov3 testing , Using the flame detection dataset , The effect is as follows :

So you can make a fire prediction ,yolov3 It's really fragrant , This time it's about github One of the pytorch Implementation version , The effect is good .

that , Next , Just come with me and practice !!!

One 、 Environmental requirements

Old rules , If a worker wants to do a good job, he must sharpen his tools first , Set up the environment !!

  • Python: 3.7.4
  • Tensorflow-GPU 1.14.0
  • Keras: 2.2.4
  • numpy:1.17.4

It is suggested to use anaconda To quickly build a virtual environment , Very fast !!!

Two 、 Data set preparation

Collect fire pictures from the Internet , And use labelimg Annotate , Get the labeled image and the location information .

as follows :

everything , Start coding!!!!

3、 ... and 、Pytorch Version of YoloV3

Pytorch_Yolov3

1. Install the module

stay requirements.txt It contains what you need this time python modular .

  • numpy
  • torch==1.2.0
  • torchvision==0.4.0
  • matplotlib
  • tensorflow==1.13.2
  • tensorboardX==2.0
  • terminaltables
  • pillow
  • tqdm

You can use pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt To install the required modules .

2. Download the required weight file

Linux Under the platform ,cd weights/, And then run bash download_weights.sh file , You can download the required weight information .

Windows Under the platform , It can be edited directly download_weigths.sh file , Copy the model links in it , Open download in Explorer .

After downloading , stay weights The contents of the document are as follows :

3. Modify the configuration file

Linux Under the platform , function cd config/ Catalog , And then run bash create_custom_model.sh <num-classes> among Is the class parameter , Modify according to your needs , Here I change it to 1.

Windows Under the platform , To configure git Of bin After the variables in the directory , function sh.exe file . after cd config After the catalog , function sh create_custom_model.sh <num-classes> that will do

After execution , modify custom.data, Modify its configuration information .

4. Configure this time yolov3 Data format

The key is coming. , The key is coming. , The key is coming. !!!

In the github Next , For custom data , It is not stated that , It's just a stroke . But it's time to yolov3 The required data format for the version of is the same as voc Format and coco The format is not the same . One for each picture txt Label information . The first column is category information , The next four columns are standardized annotation information . among label It's a category in data/custom/classes.names The index of , <> Represents the scaling factor after scaling

  • <1>*w = (xmax-xmin)/2 + xmin
  • <2>*h = (ymax-ymin)/2 + ymin
  • <3> = (xmax-xmin)/w
  • <4> = (ymax-ymin)/h

here github No data conversion provided , Two new ones here Annotations and JPEGImages Folder , Will be ready for pictures and xml Tag information in it .

And then run voc2yolov3 file , Generate train.txt and valid.txt file information , Divide the dataset into , Save the image path in two txt In file .

And then run voc_annotation.py Yes xml Tag information for processing , Deal with it as follows txt File form

And remember to modify classes.names The class name of , And copy pictures to images In file . namely

Okay , The data format is finished !!!

Now you can start training .

5. function train.py

# Training orders 
python train.py --model_def config/yolov3-custom.cfg --data_config config/custom.data --pretrained_weights weights/darknet53.conv.74
# For additional parameters, see train.py file 
# Start training where you left off 
python train.py --model_def config/yolov3-custom.cfg --data_config config/custom.data --pretrained_weights checkpoints/yolov3_ckpt_99.pth --epoch

If there is a warning solution UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.

stay model.py Calculate the location of the loss In about 192 Add the following two sentences to the left and right of the line :

obj_mask=obj_mask.bool() # convert int8 to bool

noobj_mask=noobj_mask.bool() #convert int8 to bool

The running process is shown in the figure :

It can be done by tensorboard To see what's going on .tensorboard --logdir='logs\'

6. test result

Ding Dong , Ding Dong , It's done right away !!!

python detect.py --image_folder data/imgs/ --weights_path checkpoints/yolov3_ckpt_99.pth --model_def config/yolov3-custom.cfg --class_path data/custom/classes.names

Run the above , It will be right data/imgs The images under the file are predicted , And save the prediction results to output/imgs Under the document

If it's in GPU Training on your computer , stay CPU It's predicted on the computer , It needs to be modified model.load_state_dict(torch.load(opt.weights_path, map_location='cpu'))

Okay , It's done !!! Stand up flag!!! One more tomorrow !!!

版权声明
本文为[mind_ programmonkey]所创,转载请带上原文链接,感谢

  1. 利用Python爬虫获取招聘网站职位信息
  2. Using Python crawler to obtain job information of recruitment website
  3. Several highly rated Python libraries arrow, jsonpath, psutil and tenacity are recommended
  4. Python装饰器
  5. Python实现LDAP认证
  6. Python decorator
  7. Implementing LDAP authentication with Python
  8. Vscode configures Python development environment!
  9. In Python, how dare you say you can't log module? ️
  10. 我收藏的有关Python的电子书和资料
  11. python 中 lambda的一些tips
  12. python中字典的一些tips
  13. python 用生成器生成斐波那契数列
  14. python脚本转pyc踩了个坑。。。
  15. My collection of e-books and materials about Python
  16. Some tips of lambda in Python
  17. Some tips of dictionary in Python
  18. Using Python generator to generate Fibonacci sequence
  19. The conversion of Python script to PyC stepped on a pit...
  20. Python游戏开发,pygame模块,Python实现扫雷小游戏
  21. Python game development, pyGame module, python implementation of minesweeping games
  22. Python实用工具,email模块,Python实现邮件远程控制自己电脑
  23. Python utility, email module, python realizes mail remote control of its own computer
  24. 毫无头绪的自学Python,你可能连门槛都摸不到!【最佳学习路线】
  25. Python读取二进制文件代码方法解析
  26. Python字典的实现原理
  27. Without a clue, you may not even touch the threshold【 Best learning route]
  28. Parsing method of Python reading binary file code
  29. Implementation principle of Python dictionary
  30. You must know the function of pandas to parse JSON data - JSON_ normalize()
  31. Python实用案例,私人定制,Python自动化生成爱豆专属2021日历
  32. Python practical case, private customization, python automatic generation of Adu exclusive 2021 calendar
  33. 《Python实例》震惊了,用Python这么简单实现了聊天系统的脏话,广告检测
  34. "Python instance" was shocked and realized the dirty words and advertisement detection of the chat system in Python
  35. Convolutional neural network processing sequence for Python deep learning
  36. Python data structure and algorithm (1) -- enum type enum
  37. 超全大厂算法岗百问百答(推荐系统/机器学习/深度学习/C++/Spark/python)
  38. 【Python进阶】你真的明白NumPy中的ndarray吗?
  39. All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)
  40. [advanced Python] do you really understand ndarray in numpy?
  41. 【Python进阶】Python进阶专栏栏主自述:不忘初心,砥砺前行
  42. [advanced Python] Python advanced column main readme: never forget the original intention and forge ahead
  43. python垃圾回收和缓存管理
  44. java调用Python程序
  45. java调用Python程序
  46. Python常用函数有哪些?Python基础入门课程
  47. Python garbage collection and cache management
  48. Java calling Python program
  49. Java calling Python program
  50. What functions are commonly used in Python? Introduction to Python Basics
  51. Python basic knowledge
  52. Anaconda5.2 安装 Python 库(MySQLdb)的方法
  53. Python实现对脑电数据情绪分析
  54. Anaconda 5.2 method of installing Python Library (mysqldb)
  55. Python implements emotion analysis of EEG data
  56. Master some advanced usage of Python in 30 seconds, which makes others envy it
  57. python爬取百度图片并对图片做一系列处理
  58. Python crawls Baidu pictures and does a series of processing on them
  59. python链接mysql数据库
  60. Python link MySQL database