开源！适用于Win和Linux平台的YOLO4和YOLO3( 六 ) _Linux

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 classes, coords 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:
[convolutional]filters=21[region]classes=2

Create file obj.names in the directory builddarknet\x64data, with objects names - each in new line
Create file obj.data in the directory builddarknet\x64data, containing (where classes = number of objects):

classes= 2train= data/train.txtvalid= data/test.txtnames = data/obj.namesbackup = backup/

Put image-files (.jpg) of your objects in the directory builddarknet\x64dataobj
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>
Where:

<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>
atention: <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 0.1472220 0.687109 0.379167 0.255469 0.1583331 0.420312 0.395833 0.140625 0.166667

Create file train.txt in directory builddarknet\x64data, with filenames of your images, each filename in new line, with path relative to darknet.exe, for example containing:

data/obj/img1.jpgdata/obj/img2.jpgdata/obj/img3.jpg

Download pre-trained weights for the convolutional layers and put to the directory builddarknet\x64for yolov4.cfg, yolov4-custom.cfg (162 MB): yolov4.conv.137 (Google drive mirror yolov4.conv.137 )for csresnext50-panet-spp.cfg (133 MB): csresnext50-panet-spp.conv.112for yolov3.cfg, yolov3-spp.cfg (154 MB): darknet53.conv.74for yolov3-tiny-prn.cfg , yolov3-tiny.cfg (6 MB): yolov3-tiny.conv.11for enet-coco.cfg (EfficientNetB0-Yolov3) (14 MB): enetb0-coco.conv.132
Start training by using the command line: darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137To 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 builddarknet\x64backup for each 100 iterations)(file yolo-obj_xxxx.weights will be saved to the builddarknet\x64backup 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

After training is complete - get result yolo-obj_final.weights from path builddarknet\x64backup

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 backupyolo-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