When tested the example.py code with a large input image it throws segmentation fault.
3200x2120 pixels seems to be enough to reproduce the issue.
Here's the output:
False
layer filters size input output
0 conv 16 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 16
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
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
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
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
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
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
13 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
14 conv 425 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 425
15 detection
mask_scale: Using default '1.000000'
Loading weights from ../tiny-yolo.weights...Done!
----- test original C using a file
./darknet/data/person_big.jpg: Predicted in 2.637989 seconds.
{'right': 1358, 'class': 'person', 'prob': 0.5922545194625854, 'top': 543, 'bottom': 1696, 'left': 930}
{'right': 952, 'class': 'dog', 'prob': 0.7985455989837646, 'top': 1321, 'bottom': 1750, 'left': 392}
{'right': 2960, 'class': 'horse', 'prob': 0.6815794110298157, 'top': 531, 'bottom': 1730, 'left': 2062}
----- test python API using a file
Segmentation fault (core dumped)
When tested the example.py code with a large input image it throws segmentation fault.
3200x2120 pixels seems to be enough to reproduce the issue.
Here's the output: