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.

CUDNN 5,6 support

This code is prepared for CUDNN 6. If you want to use CUDNN 5 - change cudnnSetConvolution2dDescriptor_v4 to cudnnSetConvolution2dDescriptor

Results - 4K video

Dataset #1

Input 4K video: Download input video

Camera: Samsung S7

Watch 8K comparison on Youtube:

• YOLO COCO - 4K YOLO COCO Object Detection #1

• YOLO VOC - 4K YOLO COCO Object Detection #1

• Tiny YOLO VOC -4K Tiny YOLO Object Detection #1

• YOLO 9000 - 4K YOLO9000 Object Detection #1

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Dataset #2

Input 4K video: Download input video

Camera: Samsung S7

Watch 8K comparison on Youtube:

• YOLO COCO - 4K YOLO COCO Object Detection #2

• YOLO VOC - 4K YOLO COCO Object Detection #2

• Tiny YOLO VOC - 4K Tiny YOLO Object Detection #2

• YOLO 9000 - 4K YOLO9000 Object Detection #2

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Dataset #3

Input 4K video: Download input video

Camera: Samsung S7

Watch 8K comparison on Youtube:

• YOLO COCO - 4K YOLO COCO Object Detection #3

• YOLO VOC - 4K YOLO COCO Object Detection #3

• Tiny YOLO VOC - 4K Tiny YOLO Object Detection #3

• YOLO 9000 - 4K YOLO9000 Object Detection #3

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Dataset #4

Input 4K video: Download input video

Camera: Samsung S7

Watch 8K comparison on Youtube:

• YOLO COCO - 4K YOLO COCO Object Detection #4

• YOLO VOC - 4K YOLO COCO Object Detection #4

• Tiny YOLO VOC - 4K Tiny YOLO Object Detection #4

• YOLO 9000 - 4K YOLO9000 Object Detection #4

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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