darknet is an open source neural network framework written in C and CUDA.
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darknet is an open source neural network framework written in C and CUDA. This docker image contains all the models you need to run darknet with the following neural networks and models
You can build build the docker image from the Dockerfile folder or from Docker repositories hub.
To pull the darknet image from the repo
docker pull loretoparisi/darknet
To build from this Dockerfile folder:
docker build -t darknet .
This will build all layers, cache each of them with a opportunist caching of git repositories for hunspell and dictionaries stable branches.
Then to run the container in interactive mode (bash) do
docker run --rm -it --name darknet darknet bash
then you can perform some darknet tasks like
Run yolo
# ./darknet detector test cfg/coco.data cfg/yolo.cfg /root/yolo.weights data/dog.jpg
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 reorg / 2 26 x 26 x 512 -> 13 x 13 x2048
27 route 26 24
28 conv 1024 3 x 3 / 1 13 x 13 x3072 -> 13 x 13 x1024
29 conv 425 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 425
30 detection
Loading weights from /root/yolo.weights...Done!
data/dog.jpg: Predicted in 8.208007 seconds.
car: 54%
bicycle: 51%
dog: 56%
Not compiled with OpenCV, saving to predictions.png instead
Run the rnn
root@db6641b6c335:~# cd ./darknet/
root@db6641b6c335:~/darknet# ./darknet rnn generate cfg/rnn.cfg /root/shakespeare.weights -srand 0 -seed CLEOPATRA -len 200
rnn
layer filters size input output
0 RNN Layer: 256 inputs, 1024 outputs
connected 256 -> 1024
connected 1024 -> 1024
connected 1024 -> 1024
1 RNN Layer: 1024 inputs, 1024 outputs
connected 1024 -> 1024
connected 1024 -> 1024
connected 1024 -> 1024
2 RNN Layer: 1024 inputs, 1024 outputs
connected 1024 -> 1024
connected 1024 -> 1024
connected 1024 -> 1024
3 connected 1024 -> 256
4 softmax 256
5 cost 256
Loading weights from /root/shakespeare.weights...Done!
CLEOPATRA. O, the Senate House?
These haste doth bear the studiest dangerous weeds,
Which never had more profitable mind,
And yet most woeful note to you,
That hang them in these worthiest serv
Content type
Image
Digest
Size
658 MB
Last updated
almost 10 years ago
docker pull loretoparisi/darknet