yolo v4 模型训练方法(节选自yolo v4 在github 上的文档原文)

How to train (to detect your custom objects)

(to train old Yolo v2 yolov2-voc.cfgyolov2-tiny-voc.cfgyolo-voc.cfgyolo-voc.2.0.cfg, … click by the link)

Training Yolo v4 (and v3):

  1. For training cfg/yolov4-custom.cfg download the pre-trained weights-file (162 MB): yolov4.conv.137 (Google drive mirror yolov4.conv.137 )
  2. Create file yolo-obj.cfg with the same content as in yolov4-custom.cfg (or copy yolov4-custom.cfg to yolo-obj.cfg) and:

So if classes=1 then should be filters=18. If classes=2 then write filters=21(Do not write in the cfg-file: filters=(classes + 5)x3)

(Generally filters depends on the classescoords and number of masks, i.e. filters=(classes + coords + 1)*<number of mask>, where mask is indices of anchors. If mask is absence, then filters=(classes + coords + 1)*num)

So for example, for 2 objects, your file yolo-obj.cfg should differ from yolov4-custom.cfg in such lines in each of 3 [yolo]-layers:


  1. Create file obj.names in the directory build\darknet\x64\data\, with objects names – each in new line
  2. Create file obj.data in the directory build\darknet\x64\data\, containing (where classes = number of objects):
classes = 2
train  = data/train.txt
valid  = data/test.txt
names = data/obj.names
backup = backup/
  1. Put image-files (.jpg) of your objects in the directory build\darknet\x64\data\obj\
  2. You should label each object on images from your dataset. Use this visual GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: https://github.com/AlexeyAB/Yolo_mark

It will create .txt-file for each .jpg-image-file – in the same directory and with the same name, but with .txt-extension, and put to file: object number and object coordinates on this image, for each object in new line:

<object-class> <x_center> <y_center> <width> <height>


  • <object-class> – integer object number from 0 to (classes-1)

  • <x_center> <y_center> <width> <height> – float values relative to width and height of image, it can be equal from (0.0 to 1.0]

  • for example: <x> = <absolute_x> / <image_width> or <height> = <absolute_height> / <image_height>

  • attention: <x_center> <y_center> – are center of rectangle (are not top-left corner)

    For example for img1.jpg you will be created img1.txt containing:

    1 0.716797 0.395833 0.216406 0.147222
    0 0.687109 0.379167 0.255469 0.158333
    1 0.420312 0.395833 0.140625 0.166667
  1. Create file train.txt in directory build\darknet\x64\data\, with filenames of your images, each filename in new line, with path relative to darknet.exe, for example containing:
  1. Download pre-trained weights for the convolutional layers and put to the directory build\darknet\x64

  2. Start training by using the command line: darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137

    To train on Linux use command: ./darknet detector train data/obj.data yolo-obj.cfg yolov4.conv.137 (just use ./darknet instead of darknet.exe)

    • (file yolo-obj_last.weights will be saved to the build\darknet\x64\backup\ for each 100 iterations)
    • (file yolo-obj_xxxx.weights will be saved to the build\darknet\x64\backup\ for each 1000 iterations)
    • (to disable Loss-Window use darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show, if you train on computer without monitor like a cloud Amazon EC2)
    • (to see the mAP & Loss-chart during training on remote server without GUI, use command darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map then open URL http://ip-address:8090 in Chrome/Firefox browser)

8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set valid=valid.txt or train.txt in obj.data file) and run: darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map

  1. After training is complete – get result yolo-obj_final.weights from path build\darknet\x64\backup\

    • After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just start training using: darknet.exe detector train data/obj.data yolo-obj.cfg backup\yolo-obj_2000.weights

    (in the original repository https://github.com/pjreddie/darknet the weights-file is saved only once every 10 000 iterations if(iterations > 1000))

    • Also you can get result earlier than all 45000 iterations.

Note: If during training you see nan values for avg (loss) field – then training goes wrong, but if nan is in some other lines – then training goes well.

Note: If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32.

Note: After training use such command for detection: darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights

Note: if error Out of memory occurs then in .cfg-file you should increase subdivisions=16, 32 or 64: link