why run yolov1/tiny-yolo.cfg, Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0

[issxjl2015]

./darknet detector train cfg/voc.data cfg/yolov1/tiny-yolo.cfg
tiny-yolo
layer filters size input output
0 conv 16 3 x 3 / 1 448 x 448 x 3 -> 448 x 448 x 16
1 max 2 x 2 / 2 448 x 448 x 16 -> 224 x 224 x 16
2 conv 32 3 x 3 / 1 224 x 224 x 16 -> 224 x 224 x 32
3 max 2 x 2 / 2 224 x 224 x 32 -> 112 x 112 x 32
4 conv 64 3 x 3 / 1 112 x 112 x 32 -> 112 x 112 x 64
5 max 2 x 2 / 2 112 x 112 x 64 -> 56 x 56 x 64
6 conv 128 3 x 3 / 1 56 x 56 x 64 -> 56 x 56 x 128
7 max 2 x 2 / 2 56 x 56 x 128 -> 28 x 28 x 128
8 conv 256 3 x 3 / 1 28 x 28 x 128 -> 28 x 28 x 256
9 max 2 x 2 / 2 28 x 28 x 256 -> 14 x 14 x 256
10 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512
11 max 2 x 2 / 2 14 x 14 x 512 -> 7 x 7 x 512
12 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024
13 conv 256 3 x 3 / 1 7 x 7 x1024 -> 7 x 7 x 256
14 connected 12544 -> 1470
15 Detection Layer
forced: Using default '0'
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
Loaded: 8.935500 seconds
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
1: 0.000000, 0.000000 avg, 0.001000 rate, 3.704334 seconds, 64 images
Loaded: 4.892096 seconds
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
2: 0.000000, 0.000000 avg, 0.001000 rate, 1.871806 seconds, 128 images
Loaded: 6.293604 seconds
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
3: 0.000000, 0.000000 avg, 0.001000 rate, 2.049604 seconds, 192 images
Loaded: 6.447800 seconds
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
4: 0.000000, 0.000000 avg, 0.001000 rate, 1.846883 seconds, 256 images
Loaded: 7.217837 seconds
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
5: 0.000000, 0.000000 avg, 0.001000 rate, 2.000463 seconds, 320 images
Loaded: 6.125922 seconds
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
6: 0.000000, 0.000000 avg, 0.001000 rate, 1.845230 seconds, 384 images
Loaded: 6.361571 seconds
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
Detection Avg IOU: -nan, Pos Cat: -nan, All Cat: -nan, Pos Obj: -nan, Any Obj: 0.000000, count: 0
7: 0.000000, 0.000000 avg, 0.001000 rate, 1.877696 seconds, 448 images

How to fix it? Please, help me. Thank you!

[WintonHuang]

maybe u have the wrong path that it cant get the image and "txt"

[xzy295461445]

do u solve this problem? i have the same problem.

[bohemian916]

I had the same problem. So I checkout older commit before v2 and follow the old instruction https://pjreddie.com/darknet/yolov1/

git checkout c71bff69eaf1e458850ab78a32db8aa25fee17dc
wget http://pjreddie.com/media/files/darknet.conv.weights

and edit src/yolo.c for training with custom dataset

18     char *train_images = "/home/pjreddie/data/voc/test/train.txt";
19     char *backup_directory = "/home/pjreddie/backup/";

and train

./darknet yolo train cfg/tiny-yolo.train.cfg darknet.conv.weights

it works for me

[Paul0M]

@issxjl2015 Met with the same problem when training on my own dataset. Figured out the reasion: absolute value of bbox . Fix: using relative values, <x> = <absolute_x> / <image_width> or <height> = <absolute_height> / <image_height>.