How to train YOLOv2 544x544 ? about darknet HOT 16 CLOSED

As a result:

1. There is no simple way to train Yolo on resolution larger than 416x416, but you can use .weights-file trained on 416x416, to detect on larger resolution:

   1.1. Yolo automatically resizes any images to resolution 416x416, but it makes it impossible to detect small objects

   1.2. If you want to detect small object on images with 832x832 or 1088x1088, then simply use .weights-file trained on 416x416, and change lines in your .cfg-file:

   darknet/cfg/yolo-voc.cfg

   Line 4 in 76dbdae

   height=416

From:

subdivisions=8
height=416
width=416

To:

subdivisions=64
height=1088
width=1088

In details:

• Yolo v2 has not parameter side, but has parameter num=5 (number of anchors)

  darknet/cfg/yolo-voc.cfg

  Line 232 in 76dbdae

  num=5

• and output of network: filters = (classes + coords + 1)*num

  darknet/cfg/yolo-voc.cfg

  Line 224 in 76dbdae

  filters=125

• In new Yolo v2 the number of sides is determined by a resolution network, the number of layers and its strides/steps.
• YOLO’s convolutional layers downsample the image by a factor of 32 so by using an input image of 416 we get an output feature map of 13 × 13. https://arxiv.org/pdf/1612.08242v1.pdf

And sides will automatically increased from 13 to 34. (changes of the subdivision is only necessary to reduce consumption of GPU-RAM)

On image below:

• left cfg-file (classes=6, num=5, filters=55, subdivision=8, width=416, height=416) - as you see output layer (13x13x55)
• right cfg-file (classes=6, num=5, filters=55, subdivision=64, width=1088, height=1088) - as you see output layer (34x34x55)

2. If you want to increase precision by training with higher resolution, then you can train Yolo with dynamic resolution 320x320 - 608x608 by set flag random=1:

   darknet/cfg/yolo-voc.cfg

   Line 244 in 76dbdae

   random=0

   
   This increase mAP +1%: https://arxiv.org/pdf/1612.08242v1.pdf

from darknet.

@matakk

Simply change these lines in your .cfg file: https://groups.google.com/d/msg/darknet/MumMJ2D8H9Y/UBeJOa-eCwAJ

from

[net]
batch=64
subdivisions=8
height=416
width=416

to

[net]
batch=64
subdivisions=16
height=544
width=544

If out of memory, then set subdivisions=64.
And then train by using this .cfg-file.

Also you can train for 416x416 but use with 544x544 or more, for example, 832x832.

• Trained 416x416, and detection 416x416:

• Trained 416x416, and detection 832x832:

from darknet.

If you still want to train Yolo at high resolution 1088x1088, then you can try this, but it does not provide many guarantees of success:

• change this line:

  darknet/src/detector.c

  Line 83 in 76dbdae

  int dim = (rand() % 10 + 10) * 32;

  • from: int dim = (rand() % 10 + 10) * 32;
  • to: int dim = args.w;

• set dynamic resolution flag random=1 in your .cfg-file:

  darknet/cfg/yolo-voc.cfg

  Line 244 in 76dbdae

  random=0

• change subdivisions, height and width lines in your .cfg-file:

  darknet/cfg/yolo-voc.cfg

  Line 4 in 76dbdae

  height=416

From:

subdivisions=8
height=416
width=416

To:

subdivisions=64
height=1088
width=1088

• train Yolo as usually

You should get .cfg-file look like this, if you use 6 classes(objects) and resolution 1088x1088: http://pastebin.com/GY9NPfmc

from darknet.

I have successfully trained YOLO on 544 * 544, the trick is that training images should be bigger than this size. it sacrifices the speed although, as YOLO authors mentioned on the FPS/mAP curve.

from darknet.

What needs to change in order to use a pretrained weight with 416x416 to detect with 544x544 or higher?

Thanks,

from darknet.

@kaishijeng To use a pretrained weights with 416x416 to detect with 832x832 - we need changes of the same type in your custom .cfg-file or in default yolo.cfg/yolo-voc.cfg:

from

[net]
batch=64
subdivisions=8
height=416
width=416

to

[net]
batch=64
subdivisions=64
height=832
width=832

from darknet.

from darknet.

@kaishijeng Higher resolution requires more GPU-memory.

If you get error "out of memory" then you should decrease batch-value or increase subdivisions-value. At once processed batch/subdivisions images.
64/8 requres more than 4 GB GPU-RAM for 832x832 resolution when used cuDNN, and you should use 64/64.

from darknet.

@AlexeyAB Thanks!!
I'am starting to trian with 832,but at start training ,the log has few nan value,
log:

Region Avg IOU: 0.287866, Class: 0.028420, Obj: 0.548111, No Obj: 0.513275, Avg Recall: 0.100000, count: 20
Region Avg IOU: 0.361796, Class: 0.022684, Obj: 0.525597, No Obj: 0.514241, Avg Recall: 0.333333, count: 9
Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.513698, Avg Recall: -nan, count: 0
Region Avg IOU: 0.243378, Class: 0.014114, Obj: 0.460395, No Obj: 0.513718, Avg Recall: 0.000000, count: 6
Region Avg IOU: 0.509384, Class: 0.016810, Obj: 0.515260, No Obj: 0.513929, Avg Recall: 0.500000, count: 2

Can I ignore this nan ,and continue train ?
thanks again!

from darknet.

@matakk If only in some of the lines occurs nan, I think this is normal. Try to detect each type of objects (at least each object once) after 2000-4000 iterations, and if all ok, then you can continue train.

Also note, that you should use version not earlier than 10 Jan 2017, where was fixed bug: b831db5

About nan. Nan occurs here, if count == 0:

This may be because:

• if every of 30 generated box-truth is wrong if(!truth.x) break;:

  darknet/src/region_layer.c

  Line 262 in 2fc5f6d

  if(!truth.x) break;

• or if l.batch == 0 here for (b = 0; b < l.batch; ++b), but batch is equal 64 from .cfg-file:

  darknet/src/region_layer.c

  Line 187 in 2fc5f6d

  for (b = 0; b < l.batch; ++b) {

from darknet.

@AlexeyAB
Please have a look at #30
@matakk
Have you trained on 544 * 544?

from darknet.

I tried to detect a relatively small object with 416*416 trained network and 640*480 video input, but the network can not detect it from far.

Could the reason is that because I have not included images that shows the object from far in the training/validation data-set?

from darknet.

@VanitarNordic You should not change aspect ratio, use for detection network size 608x608.

Object in training-dataset should have the same relative size in %, as in detection-dataset.

from darknet.

@AlexeyAB

No, I trained the model with 416*416 resolution

The input live video resolution is 640*480 for testing

detection-dataset you mean validation images which used in training process or you mean unseen images when we decide to test the model?

from darknet.

Detection-dataset is images or video on which you want to detect objects. Did you change network size?

What is average relative size of object was:

• in training-dataset?
• in detection-dataset?

Could the reason is that because I have not included images that shows the object from far in the training/validation data-set?

Yes.

from darknet.

Okay, I got it. Thanks.

Yes I changed the network size to 416*416 to make the speed test.

Yes, in the training data-set the object sizes are normal and are not from far, that's correct. in detection-dataset sometimes I was putting the object far from camera and it was unable to detect. I think (as you mentioned correctly) if I want to detect the object by its all scales and conditions, I should add training/validation images which cover these conditions.

from darknet.