Darknet: Open Source Neural Networks in C - Classifying With Pre-Trained Models
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. You can find the source on GitHub or you can read more about what Darknet can do right here:
https://github.com/pjreddie/darknet
ImageNet Classification
Classify images with popular models like ResNet and ResNeXt.
You can use Darknet to classify images for the 1000-class ImageNet challenge. If you haven’t installed Darknet yet, you should do that first.
http://image-net.org/challenges/LSVRC/2015/index
https://pjreddie.com/darknet/install/
Classifying With Pre-Trained Models
Here are the commands to install Darknet, download a classification weights file, and run a classifier on an image:
git clone https://github.com/pjreddie/darknet.git
cd darknet
make
wget https://pjreddie.com/media/files/extraction.weights
./darknet classifier predict cfg/imagenet1k.data cfg/extraction.cfg extraction.weights data/dog.jpg
(1) make clean
strong@foreverstrong:~/darknet_work/darknet_180906$ git clone https://github.com/pjreddie/darknet.git
Cloning into 'darknet'...
remote: Counting objects: 5878, done.
remote: Total 5878 (delta 0), reused 0 (delta 0), pack-reused 5878
Receiving objects: 100% (5878/5878), 6.11 MiB | 1.76 MiB/s, done.
Resolving deltas: 100% (3934/3934), done.
Checking connectivity... done.
strong@foreverstrong:~/darknet_work/darknet_180906$
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$ make clean
(2) Makefile
Makefile:
GPU=0
CUDNN=0
OPENCV=0
OPENMP=0
DEBUG=0
===>>>
Makefile:
GPU=1
CUDNN=1
OPENCV=0
OPENMP=0
DEBUG=0
(3) make
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$ make
(4) extraction.weights
strong@foreverstrong:~/darknet_work/darknet_180906$ cd darknet/
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$ wget https://pjreddie.com/media/files/extraction.weights
--2018-09-06 20:07:14-- https://pjreddie.com/media/files/extraction.weights
Resolving pjreddie.com (pjreddie.com)... 128.208.3.39
Connecting to pjreddie.com (pjreddie.com)|128.208.3.39|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 93821616 (89M) [application/octet-stream]
Saving to: ‘extraction.weights’
extraction.weights 100%[=================================================>] 89.47M 67.7KB/s in 23m 51s
2018-09-06 20:31:06 (64.0 KB/s) - ‘extraction.weights’ saved [93821616/93821616]
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$
(5) ./darknet classifier predict ./cfg/imagenet1k.data ./cfg/extraction.cfg ./extraction.weights ./data/dog.jpg
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$ ./darknet classifier predict ./cfg/imagenet1k.data ./cfg/extraction.cfg ./extraction.weights ./data/dog.jpg
layer filters size input output
0 conv 64 7 x 7 / 2 224 x 224 x 3 -> 112 x 112 x 64 0.236 BFLOPs
1 max 2 x 2 / 2 112 x 112 x 64 -> 56 x 56 x 64
2 conv 192 3 x 3 / 1 56 x 56 x 64 -> 56 x 56 x 192 0.694 BFLOPs
3 max 2 x 2 / 2 56 x 56 x 192 -> 28 x 28 x 192
4 conv 128 1 x 1 / 1 28 x 28 x 192 -> 28 x 28 x 128 0.039 BFLOPs
5 conv 256 3 x 3 / 1 28 x 28 x 128 -> 28 x 28 x 256 0.462 BFLOPs
6 conv 256 1 x 1 / 1 28 x 28 x 256 -> 28 x 28 x 256 0.103 BFLOPs
7 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
8 max 2 x 2 / 2 28 x 28 x 512 -> 14 x 14 x 512
9 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
10 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
11 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
12 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
13 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
14 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
15 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
16 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
17 conv 512 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 512 0.103 BFLOPs
18 conv 1024 3 x 3 / 1 14 x 14 x 512 -> 14 x 14 x1024 1.850 BFLOPs
19 max 2 x 2 / 2 14 x 14 x1024 -> 7 x 7 x1024
20 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BFLOPs
21 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BFLOPs
22 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BFLOPs
23 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BFLOPs
24 conv 1000 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x1000 0.100 BFLOPs
25 avg 7 x 7 x1000 -> 1000
26 softmax 1000
CUDA Error: out of memory
darknet: ./src/cuda.c:36: check_error: Assertion `0' failed.
Aborted (core dumped)
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$
[net]
batch=128
subdivisions=1
height=224
width=224
===>>>
[net]
# Training
#batch=128
#subdivisions=2
# Testing
batch=1
subdivisions=1
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$ ./darknet classifier predict ./cfg/imagenet1k.data ./cfg/extraction.cfg ./extraction.weights ./data/dog.jpg
layer filters size input output
0 conv 64 7 x 7 / 2 224 x 224 x 3 -> 112 x 112 x 64 0.236 BFLOPs
1 max 2 x 2 / 2 112 x 112 x 64 -> 56 x 56 x 64
2 conv 192 3 x 3 / 1 56 x 56 x 64 -> 56 x 56 x 192 0.694 BFLOPs
3 max 2 x 2 / 2 56 x 56 x 192 -> 28 x 28 x 192
4 conv 128 1 x 1 / 1 28 x 28 x 192 -> 28 x 28 x 128 0.039 BFLOPs
5 conv 256 3 x 3 / 1 28 x 28 x 128 -> 28 x 28 x 256 0.462 BFLOPs
6 conv 256 1 x 1 / 1 28 x 28 x 256 -> 28 x 28 x 256 0.103 BFLOPs
7 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
8 max 2 x 2 / 2 28 x 28 x 512 -> 14 x 14 x 512
9 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
10 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
11 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
12 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
13 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
14 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
15 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
16 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
17 conv 512 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 512 0.103 BFLOPs
18 conv 1024 3 x 3 / 1 14 x 14 x 512 -> 14 x 14 x1024 1.850 BFLOPs
19 max 2 x 2 / 2 14 x 14 x1024 -> 7 x 7 x1024
20 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BFLOPs
21 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BFLOPs
22 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BFLOPs
23 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BFLOPs
24 conv 1000 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x1000 0.100 BFLOPs
25 avg 7 x 7 x1000 -> 1000
26 softmax 1000
Loading weights from ./extraction.weights...Done!
./data/dog.jpg: Predicted in 0.005154 seconds.
12.78%: malamute
10.04%: Siberian husky
7.22%: Eskimo dog
4.91%: miniature schnauzer
4.89%: Afghan hound
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$
(6) ./darknet classifier predict ./cfg/imagenet1k.data ./cfg/darknet53_448.cfg ./darknet53_448.weights ./data/dog.jpg
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$ ./darknet classifier predict ./cfg/imagenet1k.data ./cfg/darknet53_448.cfg ./darknet53_448.weights ./data/dog.jpg
layer filters size input output
0 conv 32 3 x 3 / 1 448 x 448 x 3 -> 448 x 448 x 32 0.347 BFLOPs
1 conv 64 3 x 3 / 2 448 x 448 x 32 -> 224 x 224 x 64 1.850 BFLOPs
2 conv 32 1 x 1 / 1 224 x 224 x 64 -> 224 x 224 x 32 0.206 BFLOPs
3 conv 64 3 x 3 / 1 224 x 224 x 32 -> 224 x 224 x 64 1.850 BFLOPs
4 res 1 224 x 224 x 64 -> 224 x 224 x 64
5 conv 128 3 x 3 / 2 224 x 224 x 64 -> 112 x 112 x 128 1.850 BFLOPs
6 conv 64 1 x 1 / 1 112 x 112 x 128 -> 112 x 112 x 64 0.206 BFLOPs
7 conv 128 3 x 3 / 1 112 x 112 x 64 -> 112 x 112 x 128 1.850 BFLOPs
8 res 5 112 x 112 x 128 -> 112 x 112 x 128
9 conv 64 1 x 1 / 1 112 x 112 x 128 -> 112 x 112 x 64 0.206 BFLOPs
10 conv 128 3 x 3 / 1 112 x 112 x 64 -> 112 x 112 x 128 1.850 BFLOPs
11 res 8 112 x 112 x 128 -> 112 x 112 x 128
12 conv 256 3 x 3 / 2 112 x 112 x 128 -> 56 x 56 x 256 1.850 BFLOPs
13 conv 128 1 x 1 / 1 56 x 56 x 256 -> 56 x 56 x 128 0.206 BFLOPs
14 conv 256 3 x 3 / 1 56 x 56 x 128 -> 56 x 56 x 256 1.850 BFLOPs
15 res 12 56 x 56 x 256 -> 56 x 56 x 256
16 conv 128 1 x 1 / 1 56 x 56 x 256 -> 56 x 56 x 128 0.206 BFLOPs
17 conv 256 3 x 3 / 1 56 x 56 x 128 -> 56 x 56 x 256 1.850 BFLOPs
18 res 15 56 x 56 x 256 -> 56 x 56 x 256
19 conv 128 1 x 1 / 1 56 x 56 x 256 -> 56 x 56 x 128 0.206 BFLOPs
20 conv 256 3 x 3 / 1 56 x 56 x 128 -> 56 x 56 x 256 1.850 BFLOPs
21 res 18 56 x 56 x 256 -> 56 x 56 x 256
22 conv 128 1 x 1 / 1 56 x 56 x 256 -> 56 x 56 x 128 0.206 BFLOPs
23 conv 256 3 x 3 / 1 56 x 56 x 128 -> 56 x 56 x 256 1.850 BFLOPs
24 res 21 56 x 56 x 256 -> 56 x 56 x 256
25 conv 128 1 x 1 / 1 56 x 56 x 256 -> 56 x 56 x 128 0.206 BFLOPs
26 conv 256 3 x 3 / 1 56 x 56 x 128 -> 56 x 56 x 256 1.850 BFLOPs
27 res 24 56 x 56 x 256 -> 56 x 56 x 256
28 conv 128 1 x 1 / 1 56 x 56 x 256 -> 56 x 56 x 128 0.206 BFLOPs
29 conv 256 3 x 3 / 1 56 x 56 x 128 -> 56 x 56 x 256 1.850 BFLOPs
30 res 27 56 x 56 x 256 -> 56 x 56 x 256
31 conv 128 1 x 1 / 1 56 x 56 x 256 -> 56 x 56 x 128 0.206 BFLOPs
32 conv 256 3 x 3 / 1 56 x 56 x 128 -> 56 x 56 x 256 1.850 BFLOPs
33 res 30 56 x 56 x 256 -> 56 x 56 x 256
34 conv 128 1 x 1 / 1 56 x 56 x 256 -> 56 x 56 x 128 0.206 BFLOPs
35 conv 256 3 x 3 / 1 56 x 56 x 128 -> 56 x 56 x 256 1.850 BFLOPs
36 res 33 56 x 56 x 256 -> 56 x 56 x 256
37 conv 512 3 x 3 / 2 56 x 56 x 256 -> 28 x 28 x 512 1.850 BFLOPs
38 conv 256 1 x 1 / 1 28 x 28 x 512 -> 28 x 28 x 256 0.206 BFLOPs
39 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
40 res 37 28 x 28 x 512 -> 28 x 28 x 512
41 conv 256 1 x 1 / 1 28 x 28 x 512 -> 28 x 28 x 256 0.206 BFLOPs
42 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
43 res 40 28 x 28 x 512 -> 28 x 28 x 512
44 conv 256 1 x 1 / 1 28 x 28 x 512 -> 28 x 28 x 256 0.206 BFLOPs
45 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
46 res 43 28 x 28 x 512 -> 28 x 28 x 512
47 conv 256 1 x 1 / 1 28 x 28 x 512 -> 28 x 28 x 256 0.206 BFLOPs
48 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
49 res 46 28 x 28 x 512 -> 28 x 28 x 512
50 conv 256 1 x 1 / 1 28 x 28 x 512 -> 28 x 28 x 256 0.206 BFLOPs
51 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
52 res 49 28 x 28 x 512 -> 28 x 28 x 512
53 conv 256 1 x 1 / 1 28 x 28 x 512 -> 28 x 28 x 256 0.206 BFLOPs
54 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
55 res 52 28 x 28 x 512 -> 28 x 28 x 512
56 conv 256 1 x 1 / 1 28 x 28 x 512 -> 28 x 28 x 256 0.206 BFLOPs
57 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
58 res 55 28 x 28 x 512 -> 28 x 28 x 512
59 conv 256 1 x 1 / 1 28 x 28 x 512 -> 28 x 28 x 256 0.206 BFLOPs
60 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
61 res 58 28 x 28 x 512 -> 28 x 28 x 512
62 conv 1024 3 x 3 / 2 28 x 28 x 512 -> 14 x 14 x1024 1.850 BFLOPs
63 conv 512 1 x 1 / 1 14 x 14 x1024 -> 14 x 14 x 512 0.206 BFLOPs
64 conv 1024 3 x 3 / 1 14 x 14 x 512 -> 14 x 14 x1024 1.850 BFLOPs
65 res 62 14 x 14 x1024 -> 14 x 14 x1024
66 conv 512 1 x 1 / 1 14 x 14 x1024 -> 14 x 14 x 512 0.206 BFLOPs
67 conv 1024 3 x 3 / 1 14 x 14 x 512 -> 14 x 14 x1024 1.850 BFLOPs
68 res 65 14 x 14 x1024 -> 14 x 14 x1024
69 conv 512 1 x 1 / 1 14 x 14 x1024 -> 14 x 14 x 512 0.206 BFLOPs
70 conv 1024 3 x 3 / 1 14 x 14 x 512 -> 14 x 14 x1024 1.850 BFLOPs
71 res 68 14 x 14 x1024 -> 14 x 14 x1024
72 conv 512 1 x 1 / 1 14 x 14 x1024 -> 14 x 14 x 512 0.206 BFLOPs
73 conv 1024 3 x 3 / 1 14 x 14 x 512 -> 14 x 14 x1024 1.850 BFLOPs
74 res 71 14 x 14 x1024 -> 14 x 14 x1024
75 avg 14 x 14 x1024 -> 1024
76 conv 1000 1 x 1 / 1 1 x 1 x1024 -> 1 x 1 x1000 0.002 BFLOPs
77 softmax 1000
Loading weights from ./darknet53_448.weights...Done!
./data/dog.jpg: Predicted in 0.023981 seconds.
97.59%: malamute
0.65%: Eskimo dog
0.46%: Siberian husky
0.46%: Tibetan mastiff
0.24%: Great Pyrenees
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$
This example uses the Extraction model, you can read more about it below. After running this command you should see the following output:
https://pjreddie.com/darknet/imagenet/#extraction
0: Convolutional Layer: 224 x 224 x 3 image, 64 filters -> 112 x 112 x 64 image
1: Maxpool Layer: 112 x 112 x 64 image, 2 size, 2 stride
...
23: Convolutional Layer: 7 x 7 x 512 image, 1024 filters -> 7 x 7 x 1024 image
24: Convolutional Layer: 7 x 7 x 1024 image, 1000 filters -> 7 x 7 x 1000 image
25: Avgpool Layer: 7 x 7 x 1000 image
26: Softmax Layer: 1000 inputs
27: Cost Layer: 1000 inputs
Loading weights from extraction.weights...Done!
298 224
data/dog.jpg: Predicted in 3.756339 seconds.
malamute: 0.194782
Eskimo dog: 0.155007
Siberian husky: 0.143937
dogsled: 0.020943
miniature schnauzer: 0.020566
Darknet displays information as it loads the config file and weights, then it classifies the image and prints the top-10 classes for the image. Kelp is a mixed breed dog but she has a lot of malamute in her so we’ll consider this a success!
You can also try with other images, like the bald eagle image:
./darknet classifier predict cfg/imagenet1k.data cfg/extraction.cfg extraction.weights data/eagle.jpg
Which produces:
...
data/eagle.jpg: Predicted in 4.036698 seconds.
bald eagle: 0.797689
kite: 0.185116
vulture: 0.006402
prairie chicken: 0.001041
hen: 0.000888
Pretty good!
If you don’t specify an image file you will be prompted at run-time for an image. This way you can classify multiple in a row without reloading the whole model. Use the command:
./darknet classifier predict cfg/imagenet1k.data cfg/extraction.cfg extraction.weights
Then you will get a prompt that looks like:
....
27: Softmax Layer: 1000 inputs
28: Cost Layer: 1000 inputs
Loading weights from extraction.weights...Done!
Enter Image Path:
Whenever you get bored of classifying images you can use to exit the program.
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$ ./darknet classifier predict ./cfg/imagenet1k.data ./cfg/extraction.cfg ./extraction.weights
layer filters size input output
0 conv 64 7 x 7 / 2 224 x 224 x 3 -> 112 x 112 x 64 0.236 BFLOPs
1 max 2 x 2 / 2 112 x 112 x 64 -> 56 x 56 x 64
2 conv 192 3 x 3 / 1 56 x 56 x 64 -> 56 x 56 x 192 0.694 BFLOPs
3 max 2 x 2 / 2 56 x 56 x 192 -> 28 x 28 x 192
4 conv 128 1 x 1 / 1 28 x 28 x 192 -> 28 x 28 x 128 0.039 BFLOPs
5 conv 256 3 x 3 / 1 28 x 28 x 128 -> 28 x 28 x 256 0.462 BFLOPs
6 conv 256 1 x 1 / 1 28 x 28 x 256 -> 28 x 28 x 256 0.103 BFLOPs
7 conv 512 3 x 3 / 1 28 x 28 x 256 -> 28 x 28 x 512 1.850 BFLOPs
8 max 2 x 2 / 2 28 x 28 x 512 -> 14 x 14 x 512
9 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
10 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
11 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
12 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
13 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
14 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
15 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BFLOPs
16 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BFLOPs
17 conv 512 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 512 0.103 BFLOPs
18 conv 1024 3 x 3 / 1 14 x 14 x 512 -> 14 x 14 x1024 1.850 BFLOPs
19 max 2 x 2 / 2 14 x 14 x1024 -> 7 x 7 x1024
20 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BFLOPs
21 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BFLOPs
22 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BFLOPs
23 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BFLOPs
24 conv 1000 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x1000 0.100 BFLOPs
25 avg 7 x 7 x1000 -> 1000
26 softmax 1000
Loading weights from ./extraction.weights...Done!
Enter Image Path: data/dog.jpg
data/dog.jpg: Predicted in 0.005104 seconds.
12.78%: malamute
10.04%: Siberian husky
7.22%: Eskimo dog
4.91%: miniature schnauzer
4.89%: Afghan hound
Enter Image Path: data/eagle.jpg
data/eagle.jpg: Predicted in 0.020505 seconds.
61.72%: bald eagle
36.87%: kite
0.48%: vulture
0.19%: ptarmigan
0.14%: hen
Enter Image Path: ^C
strong@foreverstrong:~/darknet_work/darknet_180906/darknet$
Wordbook
you only look once,YOLO
Visual Object Classes,VOC
Pattern Analysis, Statistical Modelling and Computational Learning,PASCAL
mean Average Precision,mAP:平均精度均值
floating point operations per second,FLOPS
frame rate or frame frequency, frames per second,FPS
hertz,Hz
billion,Bn
operations,Ops
configuration,cfg
ImageNet Large Scale Visual Recognition Challenge,ILSVRC
Microsoft Common Objects in Context,MS COCO