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YOLOv4 APP inference result.png


The YOLOv4 APP can be applied to factory defect detection, medical image analysis, biological image analysis, industrial safety image analysis, mask image analysis, etc.


Russian Alexey Bochkovskiy, the maintainer of YOLO Darknet, discovered that the CSPNet detector developed by Wang Jianyao of the Institute of Information Technology of Academia Sinica and Director Liao Hongyuan was fast and good, so he invited Academia Sinica to develop YOLOv4 as a backbone. Various improvements have been made to the previous generation YOLOv3, which not only maintains a certain detection speed, but also greatly improves detection accuracy and reduces hardware usage.


[Operation steps and instructions]

Before using YOLOv4 training or object detection, please make sure that the dataset folder is selected correctly.

The "browse" button next to the "Select Dataset" field can open the data folder location for the user to confirm and modify.


YOLOv4 APP Select dataset.png



Start preparing pre-processing, 1~5 of "Prepare" are the pre-processing part, respectively:

1.annotation pascal voc xml:Open the annotation webpage for image annotation.

2.convert yolo format:Convert the voc xml tag file into yolo format. Before pressing this button, please confirm whether the label.names category in the selected data set is filled in correctly.

3.prepare train txt: Generate train.txt.

4.prepare val txt: Generate valid.txt.

5.prepare config file: Open voc.data. If there is an increase or decrease in categories, please be sure to modify the number of classes. If there are 5 categories, fill in 5 (not counting background), and so on.

YOLOv4 APP dataset preprocessing area.png

YOLOv4 APP Open annotation page.png

YOLOv4 APP generates yolov4 format.png

YOLOv4 APP generates train txt.png

YOLOv4 APP generates valid txt.png


In addition to modifying the category, if the selected data set changes, you also need to change the path of train, valid, names, and backup.


YOLOv4 APP modify YOLOv4 cfg classes.png



After finishing the pre-processing, you can start training. Training is divided into GPU and CPU. Please choose GPU or CPU mode according to your hardware.


Note: If you change the number of categories, please modify yolov4.cfg again. For details, please refer to the relevant settings of "99.edit yolov4.cfg".


Note: If you want to modify the size of the training image, such as 512 x 512, you must modify yolov4.cfg. For details, please see "99.edit yolov4.cfg" related settings.


YOLOv4 APP train area.png

YOLOv4 APP training.png



There are several ways to infer, please choose GPU or CPU mode according to your hardware:

7.inference: inference a single image.

8.inference webcam: Inference webcam image.

9.inference folder: Inference all images in the test/images folder under the selected dataset folder.

10.inference api: Use the web page for image inference, press the “10.inference api” button, and then press the “11.inference api browser” button to open the web page.


YOLOv4 APP inference area.png

YOLOv4 APP inference a single image.png

YOLOv4 APP inference webcam.png

YOLOv4 APP inference folder.png

YOLOv4 APP inference api.png



99.edit yolov4.cfg: Edit the yolov4.cfg file. If there are any increase or decrease of categories, please modify the classes and filters in the three positions in yolov4.cfg.

99.browse data: Open the data folder.

99.auto labeling: Automatically label the images in the test folder, and output the labeling file to the auto_labeling folder.

99.calculate anchors: Use the training and verification data set to calculate the appropriate anchor points. If you want to modify the anchor points, please modify them in the three positions of yolov4.cfg.


YOLOv4 APP other area.png

YOLOv4 APP Open yolov4 cfg.png

YOLOv4 APP modify YOLOv4 cfg-1.png

YOLOv4 APP modify YOLOv4 cfg-2.png

YOLOv4 APP modify YOLOv4 cfg-3.png


If you need to modify the size of the training image, you need to modify the width and height.


YOLOv4 APP modify YOLOv4 cfg-4.png


YOLOv4 APP calculates anchor.png


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