Darknet

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

For more information see the Darknet project website.

Results - 4K video

YOLO COCO

YOLO VOC

Tiny YOLO VOC

...

YOLO 9000

...

Webcam Demo!

Try it yourself:

1. Build darknet
2. Download weights
3. Run webcam demos:

./webcam-coco.sh
./webcam-tiny-yolo.sh
./webcam-voc.sh
./webcam-yolo9000.sh

Build darknet

Edit Makefile to enable GPU, CUDNN and OpenCV:

GPU=1
CUDNN=1
OPENCV=1
DEBUG=0

Choose your CUDA architecture, example for GTX980M: (you can check it here CUDA Compute Capability)

ARCH=       -gencode arch=compute_52,code=[sm_52,compute_52]

Run make and you are ready!

Download weights

All weights are available at Darknet project website.

cd weights
wget https://pjreddie.com/media/files/yolo-voc.weights
wget -O yolo-coco.weights https://pjreddie.com/media/files/yolo.weights
wget https://pjreddie.com/media/files/tiny-yolo-voc.weights
wget https://pjreddie.com/media/files/yolo9000.weights

Architectures

YOLO COCO

	layer	filters	size	input	output
0	conv	32	3 x 3 / 1	608 x 608 x 3	608 x 608 x 32
1	max		2 x 2 / 2	608 x 608 x 32	304 x 304 x 32
2	conv	64	3 x 3 / 1	304 x 304 x 32	304 x 304 x 64
3	max		2 x 2 / 2	304 x 304 x 64	152 x 152 x 64
4	conv	128	3 x 3 / 1	152 x 152 x 64	152 x 152 x 128
5	conv	64	1 x 1 / 1	152 x 152 x 128	152 x 152 x 64
6	conv	128	3 x 3 / 1	152 x 152 x 64	152 x 152 x 128
7	max		2 x 2 / 2	152 x 152 x 128	76 x 76 x 128
8	conv	256	3 x 3 / 1	76 x 76 x 128	76 x 76 x 256
9	conv	128	1 x 1 / 1	76 x 76 x 256	76 x 76 x 128
10	conv	256	3 x 3 / 1	76 x 76 x 128	76 x 76 x 256
11	max		2 x 2 / 2	76 x 76 x 256	38 x 38 x 256
12	conv	512	3 x 3 / 1	38 x 38 x 256	38 x 38 x 512
13	conv	256	1 x 1 / 1	38 x 38 x 512	38 x 38 x 256
14	conv	512	3 x 3 / 1	38 x 38 x 256	38 x 38 x 512
15	conv	256	1 x 1 / 1	38 x 38 x 512	38 x 38 x 256
16	conv	512	3 x 3 / 1	38 x 38 x 256	38 x 38 x 512
17	max		2 x 2 / 2	38 x 38 x 512	19 x 19 x 512
18	conv	1024	3 x 3 / 1	19 x 19 x 512	19 x 19 x1024
19	conv	512	1 x 1 / 1	19 x 19 x1024	19 x 19 x 512
20	conv	1024	3 x 3 / 1	19 x 19 x 512	19 x 19 x1024
21	conv	512	1 x 1 / 1	19 x 19 x1024	19 x 19 x 512
22	conv	1024	3 x 3 / 1	19 x 19 x 512	19 x 19 x1024
23	conv	1024	3 x 3 / 1	19 x 19 x1024	19 x 19 x1024
24	conv	1024	3 x 3 / 1	19 x 19 x1024	19 x 19 x1024
25	route	16
26	conv	64	1 x 1 / 1	38 x 38 x 512	38 x 38 x 64
27	reorg		/ 2	38 x 38 x 64	19 x 19 x 256
28	route	27 24
29	conv	1024	3 x 3 / 1	19 x 19 x1280	19 x 19 x1024
30	conv	425	1 x 1 / 1	19 x 19 x1024	19 x 19 x 425
31	detection

YOLO VOC

	layer	filters	size	input	output
0	conv	32	3 x 3 / 1	416 x 416 x 3	416 x 416 x 32
1	max		2 x 2 / 2	416 x 416 x 32	208 x 208 x 32
2	conv	64	3 x 3 / 1	208 x 208 x 32	208 x 208 x 64
3	max		2 x 2 / 2	208 x 208 x 64	104 x 104 x 64
4	conv	128	3 x 3 / 1	104 x 104 x 64	104 x 104 x 128
5	conv	64	1 x 1 / 1	104 x 104 x 128	104 x 104 x 64
6	conv	128	3 x 3 / 1	104 x 104 x 64	104 x 104 x 128
7	max		2 x 2 / 2	104 x 104 x 128	52 x 52 x 128
8	conv	256	3 x 3 / 1	52 x 52 x 128	52 x 52 x 256
9	conv	128	1 x 1 / 1	52 x 52 x 256	52 x 52 x 128
10	conv	256	3 x 3 / 1	52 x 52 x 128	52 x 52 x 256
11	max		2 x 2 / 2	52 x 52 x 256	26 x 26 x 256
12	conv	512	3 x 3 / 1	26 x 26 x 256	26 x 26 x 512
13	conv	256	1 x 1 / 1	26 x 26 x 512	26 x 26 x 256
14	conv	512	3 x 3 / 1	26 x 26 x 256	26 x 26 x 512
15	conv	256	1 x 1 / 1	26 x 26 x 512	26 x 26 x 256
16	conv	512	3 x 3 / 1	26 x 26 x 256	26 x 26 x 512
17	max		2 x 2 / 2	26 x 26 x 512	13 x 13 x 512
18	conv	1024	3 x 3 / 1	13 x 13 x 512	13 x 13 x1024
19	conv	512	1 x 1 / 1	13 x 13 x1024	13 x 13 x 512
20	conv	1024	3 x 3 / 1	13 x 13 x 512	13 x 13 x1024
21	conv	512	1 x 1 / 1	13 x 13 x1024	13 x 13 x 512
22	conv	1024	3 x 3 / 1	13 x 13 x 512	13 x 13 x1024
23	conv	1024	3 x 3 / 1	13 x 13 x1024	13 x 13 x1024
24	conv	1024	3 x 3 / 1	13 x 13 x1024	13 x 13 x1024
25	route	16
26	conv	64	1 x 1 / 1	26 x 26 x 512	26 x 26 x 64
27	reorg		/ 2	26 x 26 x 64	13 x 13 x 256
28	route	27 24
29	conv	1024	3 x 3 / 1	13 x 13 x1280	13 x 13 x1024
30	conv	125	1 x 1 / 1	13 x 13 x1024	13 x 13 x 125
31	detection

Tiny YOLO VOC

	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	125	1 x 1 / 1	13 x 13 x1024	13 x 13 x 125
15	detection

YOLO 9000

	layer	filters	size	input	output
0	conv	32	3 x 3 / 1	544 x 544 x 3	544 x 544 x 32
1	max		2 x 2 / 2	544 x 544 x 32	272 x 272 x 32
2	conv	64	3 x 3 / 1	272 x 272 x 32	272 x 272 x 64
3	max		2 x 2 / 2	272 x 272 x 64	136 x 136 x 64
4	conv	128	3 x 3 / 1	136 x 136 x 64	136 x 136 x 128
5	conv	64	1 x 1 / 1	136 x 136 x 128	136 x 136 x 64
6	conv	128	3 x 3 / 1	136 x 136 x 64	136 x 136 x 128
7	max		2 x 2 / 2	136 x 136 x 128	68 x 68 x 128
8	conv	256	3 x 3 / 1	68 x 68 x 128	68 x 68 x 256
9	conv	128	1 x 1 / 1	68 x 68 x 256	68 x 68 x 128
10	conv	256	3 x 3 / 1	68 x 68 x 128	68 x 68 x 256
11	max		2 x 2 / 2	68 x 68 x 256	34 x 34 x 256
12	conv	512	3 x 3 / 1	34 x 34 x 256	34 x 34 x 512
13	conv	256	1 x 1 / 1	34 x 34 x 512	34 x 34 x 256
14	conv	512	3 x 3 / 1	34 x 34 x 256	34 x 34 x 512
15	conv	256	1 x 1 / 1	34 x 34 x 512	34 x 34 x 256
16	conv	512	3 x 3 / 1	34 x 34 x 256	34 x 34 x 512
17	max		2 x 2 / 2	34 x 34 x 512	17 x 17 x 512
18	conv	1024	3 x 3 / 1	17 x 17 x 512	17 x 17 x1024
19	conv	512	1 x 1 / 1	17 x 17 x1024	17 x 17 x 512
20	conv	1024	3 x 3 / 1	17 x 17 x 512	17 x 17 x1024
21	conv	512	1 x 1 / 1	17 x 17 x1024	17 x 17 x 512
22	conv	1024	3 x 3 / 1	17 x 17 x 512	17 x 17 x1024
23	conv	28269	1 x 1 / 1	17 x 17 x1024	17 x 17 x28269
24	detection