YoloV2 Custom Training from own Dataset

How to train (to detect your custom objects):

1. Create file yolov2-custom.cfg with the same content as in yolo-voc.2.0.cfg (or copy yolo-voc.2.0.cfg to yolov2-custom.cfg) and:

• change line batch to batch=64
• change line subdivisions to subdivisions=8
• change line classes=20 to your number of objects
• change line #237 from filters=125 to: filters=(classes + 5)x5, so if classes=2 then should be filters=35. Or if you use classes=3 then write filters=40, do not write in the cfg-file: filters=(classes + 5)x5.

(Generally filters depends on the classes, num and coords, i.e. equal to (classes + coords + 1)*num, where num is number of anchors)

So for example, for 2 objects, your file yolo-obj.cfg should differ from yolo-voc.2.0.cfg in such lines:

[convolutional]
filters=35

[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. 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> <y> <width> <height>

Where:

• <object-class> - integer number of object from 0 to (classes-1)
• <x> <y> <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> <y> - are center of rectangle (are not top-left corner)

For example for img1.jpg you should create 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 (76 MB): http://pjreddie.com/media/files/darknet19_448.conv.23 and put to the directory build\darknet\x64

8. Start training by using the command line: darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23

   (file yolo-obj_xxx.weights will be saved to the build\darknet\x64\backup\ for each 100 iterations) (To disable Loss-Window use darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23 -dont_show, if you train on computer without monitor like a cloud Amazaon EC2)

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

• After each 1000 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy yolo-obj_2000.weights from build\darknet\x64\backup\ to build\darknet\x64\ and start training using: darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights

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

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 default weights file for tiny-yolo-voc: http://pjreddie.com/media/files/tiny-yolo-voc.weights
• Get pre-trained weights tiny-yolo-voc.conv.13 using command: darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13
• Make your custom model tiny-yolo-obj.cfg based on tiny-yolo-voc.cfg instead of yolo-voc.2.0.cfg
• Start training: darknet.exe detector train data/obj.data tiny-yolo-obj.cfg tiny-yolo-voc.conv.13

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.