chuanenlin / drone-net Goto Github PK
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https://towardsdatascience.com/tutorial-build-an-object-detection-system-using-yolo-9a930513643a
I don't see where it's referenced. How does darknet know to use that dir?
You should specify the labeling format. If it is <class_id> , the bounding boxes are completely broken.
Sorry to bother you, but I have a question how did you convert the cpkg files into a standalone weight?
Hi, I've followed your tutorial in order to train Tiny YOLOv3 with drone images. After ~1900 iterations I'm still not getting any detection even on training images, which is strange. I've tested with your weights and everything works, so there must be some problem during my training procedure.
Darknet has been compiled with GPU and the training has been done on Google Colab. Here is the output during the training run with the command ./darknet detector train drone.data cfg/yolov3-tiny-drone.cfg darknet53.conv.74
:
layer filters size input output
0 conv 16 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BFLOPs
1 max 2 x 2 / 2 416 x 416 x 16 -> 208 x 208 x 16
2 conv 32 3 x 3 / 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BFLOPs
3 max 2 x 2 / 2 208 x 208 x 32 -> 104 x 104 x 32
4 conv 64 3 x 3 / 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BFLOPs
5 max 2 x 2 / 2 104 x 104 x 64 -> 52 x 52 x 64
6 conv 128 3 x 3 / 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BFLOPs
7 max 2 x 2 / 2 52 x 52 x 128 -> 26 x 26 x 128
8 conv 256 3 x 3 / 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BFLOPs
9 max 2 x 2 / 2 26 x 26 x 256 -> 13 x 13 x 256
10 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
11 max 2 x 2 / 1 13 x 13 x 512 -> 13 x 13 x 512
12 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
13 conv 256 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BFLOPs
14 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
15 conv 18 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 18 0.003 BFLOPs
16 yolo
17 route 13
18 conv 128 1 x 1 / 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BFLOPs
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8
21 conv 256 3 x 3 / 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BFLOPs
22 conv 18 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 18 0.006 BFLOPs
23 yolo
Loading weights from darknet53.conv.74...Done!
yolov3-tiny-drone
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
Resizing
416
Loaded: 0.000073 seconds
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499833, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498678, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499832, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498684, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499832, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498682, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499833, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498673, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499832, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498681, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499833, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498680, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499832, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498676, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499833, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498681, .5R: -nan, .75R: -nan, count: 0
1: 315.492737, 315.492737 avg, 0.000000 rate, 1.219311 seconds, 24 images
Loaded: 0.000067 seconds
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499833, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498674, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499833, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498679, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499832, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498677, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499833, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498676, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499833, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498684, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499832, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498684, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499833, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498679, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.499832, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.498675, .5R: -nan, .75R: -nan, count: 0
2: 315.492767, 315.492737 avg, 0.000000 rate, 0.481109 seconds, 48 images
................
Loaded: 0.000072 seconds
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.235015, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.036456, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.235015, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.036457, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.235015, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.036457, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.235015, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.036458, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.235015, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.036456, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.235015, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.036454, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.235015, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.036455, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.235015, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.036453, .5R: -nan, .75R: -nan, count: 0
379: 26.248299, 33.163815 avg, 0.000021 rate, 0.740130 seconds, 9096 images
Loaded: 0.000068 seconds
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.232247, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.034958, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.232247, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.034960, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.232247, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.034961, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.232246, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.034962, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.232248, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.034957, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.232247, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.034961, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.232247, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.034960, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.232248, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.034960, .5R: -nan, .75R: -nan, count: 0
380: 25.502501, 32.397682 avg, 0.000021 rate, 0.755673 seconds, 9120 images
Resizing
544
..................
Loaded: 0.000052 seconds
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000022, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000003, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000022, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000003, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000022, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000003, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000022, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000003, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000022, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000003, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000022, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000003, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000022, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000003, .5R: -nan, .75R: -nan, count: 0
Region 16 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000022, .5R: -nan, .75R: -nan, count: 0
Region 23 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000003, .5R: -nan, .75R: -nan, count: 0
1930: 0.000000, 0.002069 avg, 0.001000 rate, 0.583884 seconds, 46320 images
Do you notice anything strange that might be related to the problem I'm facing?
Thank you so much for your help and for sharing the tutorial
Edit
Here is a link to the avg loss plot of another run of training which gives the same problem: https://imgur.com/8IG3yO4
Hi, I'm wondering what pre-trained weight you used for training to generate the yolo-drone-weights, was it yolov3-tiny/yolov2/voc?
Also, did you do any pre-processing for the images before training?
how do you think, what is the minimum train batch size?
Hi, I'm using default cfg and weight for drone detection. However, I'm not getting accurate results. In fact, there are multiple drones are getting detected in a single yolo bounding box.
Here are some of the result on the training images:
I even trained the network from scratch(MSE Loss ~ 0.18) but still no improvement. I'm using the Yolov4 branch from AlexeyAB/darknet, which expects a normalised coordinate and I'm also using normalised coordinate for training. What would be the reason for my error? Thanks!
Quick question for the label files.
At yolo doc it says:
Where x, y, width, and height are relative to the image's width and height.
In your label files you have the real coordinates (x,y for the center of the image) and width/height, is that correct?
When i run darknet to train my custom set of images ,
I get the following error
darknet: ./src/parser.c:315: parse_yolo: Assertion `l.outputs == params.inputs' failed.
Aborted (core dumped)
ubuntu couldn't open drone.data file
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