partheee/yolov4_darknet_custom_object_detection

Yolo v4 for Windows and Linux

1. How to train (to detect your custom objects)
2. How to train tiny-yolo (to detect your custom objects)

How to train (to detect your custom objects)

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

Training Yolo v4 (and v3):

0. For training cfg/yolov4-custom.cfg download the pre-trained weights-file (162 MB): yolov4.conv.137 (Google drive mirror yolov4.conv.137 )

1. Create file yolo-obj.cfg with the same content as in yolov4-custom.cfg (or copy yolov4-custom.cfg to yolo-obj.cfg) and:

• change line batch to [batch=64]
• change line subdivisions to [subdivisions=16]
• change line max_batches to (classes*2000 but not less than number of training images, but not less than number of training images and not less than 6000), f.e. [max_batches=6000] if you train for 3 classes
• change line steps to 80% and 90% of max_batches, f.e. [steps=4800,5400]
• set network size width=416 height=416 or any value multiple of 32
• change line classes=80 to your number of objects in each of 3 [yolo]-layers:
• change [filters=255] to filters=(classes + 5)x3 in the 3 [convolutional] before each [yolo] layer, keep in mind that it only has to be the last [convolutional] before each of the [yolo] layers.

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

2. Create file obj.names in the directory build\darknet\x64\data\, with objects names - each in new line

3. 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/

4. Put image-files (.jpg) of your objects in the directory build\darknet\x64\data\obj\

5. 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 0.147222
0 0.687109 0.379167 0.255469 0.158333
1 0.420312 0.395833 0.140625 0.166667

6. 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:

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

7. Download pre-trained weights for the convolutional layers and put to the directory build\darknet\x64

   • for yolov4.cfg, yolov4-custom.cfg (162 MB): yolov4.conv.137 (Google drive mirror yolov4.conv.137 )
   • for yolov4-tiny.cfg, yolov4-tiny-3l.cfg, yolov4-tiny-custom.cfg (19 MB): yolov4-tiny.conv.29
   • for csresnext50-panet-spp.cfg (133 MB): csresnext50-panet-spp.conv.112
   • for yolov3.cfg, yolov3-spp.cfg (154 MB): darknet53.conv.74
   • for yolov3-tiny-prn.cfg , yolov3-tiny.cfg (6 MB): yolov3-tiny.conv.11
   • for enet-coco.cfg (EfficientNetB0-Yolov3) (14 MB): enetb0-coco.conv.132

8. 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

9. 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

How to train tiny-yolo (to detect your custom objects):

Do all the same steps as for the full yolo model as described above. With the exception of:

• Download file with the first 29-convolutional layers of yolov4-tiny: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29 (Or get this file from yolov4-tiny.weights file by using command: darknet.exe partial cfg/yolov4-tiny-custom.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29
• Make your custom model yolov4-tiny-obj.cfg based on cfg/yolov4-tiny-custom.cfg instead of yolov4.cfg
• Start training: darknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29

For training Yolo based on other models (DenseNet201-Yolo or ResNet50-Yolo), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights.

Credits

AlexeyAB