jing-interactive / Dancinggaga
Licence: mit
AI 尬舞机
Stars: ✭ 315
Projects that are alternatives of or similar to Dancinggaga
Hey Jetson
Deep Learning based Automatic Speech Recognition with attention for the Nvidia Jetson.
Stars: ✭ 161 (-48.89%)
Mutual labels: deep-neural-networks, inference
Awesome Emdl
Embedded and mobile deep learning research resources
Stars: ✭ 554 (+75.87%)
Mutual labels: deep-neural-networks, inference
Awesome System For Machine Learning
A curated list of research in machine learning system. I also summarize some papers if I think they are really interesting.
Stars: ✭ 1,185 (+276.19%)
Mutual labels: deep-neural-networks, inference
Bmw Yolov4 Inference Api Cpu
This is a repository for an nocode object detection inference API using the Yolov4 and Yolov3 Opencv.
Stars: ✭ 180 (-42.86%)
Mutual labels: deep-neural-networks, inference
Adversarial Robustness Toolbox
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Stars: ✭ 2,638 (+737.46%)
Mutual labels: deep-neural-networks, inference
Chaidnn
HLS based Deep Neural Network Accelerator Library for Xilinx Ultrascale+ MPSoCs
Stars: ✭ 258 (-18.1%)
Mutual labels: deep-neural-networks, inference
Models
Model Zoo for Intel® Architecture: contains Intel optimizations for running deep learning workloads on Intel® Xeon® Scalable processors
Stars: ✭ 248 (-21.27%)
Mutual labels: deep-neural-networks, inference
Bmw Tensorflow Inference Api Gpu
This is a repository for an object detection inference API using the Tensorflow framework.
Stars: ✭ 277 (-12.06%)
Mutual labels: deep-neural-networks, inference
Parakeet
PAddle PARAllel text-to-speech toolKIT (supporting WaveFlow, WaveNet, Transformer TTS and Tacotron2)
Stars: ✭ 279 (-11.43%)
Mutual labels: deep-neural-networks
Cascaded Fcn
Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"
Stars: ✭ 296 (-6.03%)
Mutual labels: deep-neural-networks
Filetype.py
Small, dependency-free, fast Python package to infer binary file types checking the magic numbers signature
Stars: ✭ 275 (-12.7%)
Mutual labels: inference
Bigdata18
Transfer learning for time series classification
Stars: ✭ 284 (-9.84%)
Mutual labels: deep-neural-networks
Deep Learning Uncertainty
Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.
Stars: ✭ 296 (-6.03%)
Mutual labels: deep-neural-networks
Awesome Distributed Deep Learning
A curated list of awesome Distributed Deep Learning resources.
Stars: ✭ 277 (-12.06%)
Mutual labels: deep-neural-networks
Attention is all you need
Transformer of "Attention Is All You Need" (Vaswani et al. 2017) by Chainer.
Stars: ✭ 303 (-3.81%)
Mutual labels: deep-neural-networks
Model Compression Papers
Papers for deep neural network compression and acceleration
Stars: ✭ 296 (-6.03%)
Mutual labels: deep-neural-networks
Pose Residual Network Pytorch
Code for the Pose Residual Network introduced in 'MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network' paper https://arxiv.org/abs/1807.04067
Stars: ✭ 277 (-12.06%)
Mutual labels: deep-neural-networks
Tensorflow Image Detection
A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception.
Stars: ✭ 306 (-2.86%)
Mutual labels: deep-neural-networks
Yolo V2 Pytorch
YOLO for object detection tasks
Stars: ✭ 302 (-4.13%)
Mutual labels: deep-neural-networks
Adversarial Examples Pytorch
Implementation of Papers on Adversarial Examples
Stars: ✭ 293 (-6.98%)
Mutual labels: deep-neural-networks
DancingGaga
Openpose implementation using darknet framework, originated from openpose-darknet
Result
Steps to build from Visual Studio 2015
- First you need to build lightnet
git clone --recurse-submodules https://github.com/jing-vision/lightnet.git
- Follow lightnet's building steps
- Then you need to have premake installed and execute
DancingGaga/gen-vs2015.bat
to generateDancingGaga/vs2015
folder - You can find
DancingGaga/vs2015/DancingGaga.sln
, you should be able to build it w/o errors. (If you are lucky like me.)
Steps to run
-
Download weight file and copy it as
bin/openpose.weight
-
Usage
DancingGaga.exe -cfg=[openpose.cfg] -weights=[openpose.weight] media-source
e.g you can detect pose from a video
DancingGaga.exe pickme-101.mp4
Or from an image
DancingGaga.exe person.jpg
Or even from your default camera (index #0)
DancingGaga.exe 0
- Other network models
DancingGaga.exe -cfg=..\coco.cfg -weights=..\coco.weights person.jpg
DancingGaga.exe -cfg=..\mpi.cfg -weights=..\mpi.weights person.jpg
DancingGaga.exe -cfg=..\body_25.cfg -weights=..\body_25.weights person.jpg
network layout
layer filters size input output
0 conv 64 3 x 3 / 1 200 x 200 x 3 -> 200 x 200 x 64 0.138 BF
1 conv 64 3 x 3 / 1 200 x 200 x 64 -> 200 x 200 x 64 2.949 BF
2 max 2 x 2 / 2 200 x 200 x 64 -> 100 x 100 x 64 0.003 BF
3 conv 128 3 x 3 / 1 100 x 100 x 64 -> 100 x 100 x 128 1.475 BF
4 conv 128 3 x 3 / 1 100 x 100 x 128 -> 100 x 100 x 128 2.949 BF
5 max 2 x 2 / 2 100 x 100 x 128 -> 50 x 50 x 128 0.001 BF
6 conv 256 3 x 3 / 1 50 x 50 x 128 -> 50 x 50 x 256 1.475 BF
7 conv 256 3 x 3 / 1 50 x 50 x 256 -> 50 x 50 x 256 2.949 BF
8 conv 256 3 x 3 / 1 50 x 50 x 256 -> 50 x 50 x 256 2.949 BF
9 conv 256 3 x 3 / 1 50 x 50 x 256 -> 50 x 50 x 256 2.949 BF
10 max 2 x 2 / 2 50 x 50 x 256 -> 25 x 25 x 256 0.001 BF
11 conv 512 3 x 3 / 1 25 x 25 x 256 -> 25 x 25 x 512 1.475 BF
12 conv 512 3 x 3 / 1 25 x 25 x 512 -> 25 x 25 x 512 2.949 BF
13 conv 256 3 x 3 / 1 25 x 25 x 512 -> 25 x 25 x 256 1.475 BF
14 conv 128 3 x 3 / 1 25 x 25 x 256 -> 25 x 25 x 128 0.369 BF
15 conv 128 3 x 3 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.184 BF
16 conv 128 3 x 3 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.184 BF
17 conv 128 3 x 3 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.184 BF
18 conv 512 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 512 0.082 BF
19 conv 38 1 x 1 / 1 25 x 25 x 512 -> 25 x 25 x 38 0.024 BF
20 route 14
21 conv 128 3 x 3 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.184 BF
22 conv 128 3 x 3 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.184 BF
23 conv 128 3 x 3 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.184 BF
24 conv 512 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 512 0.082 BF
25 conv 19 1 x 1 / 1 25 x 25 x 512 -> 25 x 25 x 19 0.012 BF
26 route 19 25 14
27 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
28 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
29 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
30 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
31 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
32 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
33 conv 38 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 38 0.006 BF
34 route 26
35 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
36 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
37 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
38 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
39 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
40 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
41 conv 19 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 19 0.003 BF
42 route 33 41 14
43 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
44 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
45 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
46 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
47 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
48 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
49 conv 38 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 38 0.006 BF
50 route 42
51 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
52 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
53 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
54 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
55 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
56 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
57 conv 19 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 19 0.003 BF
58 route 49 57 14
59 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
60 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
61 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
62 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
63 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
64 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
65 conv 38 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 38 0.006 BF
66 route 58
67 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
68 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
69 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
70 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
71 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
72 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
73 conv 19 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 19 0.003 BF
74 route 65 73 14
75 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
76 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
77 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
78 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
79 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
80 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
81 conv 38 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 38 0.006 BF
82 route 74
83 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
84 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
85 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
86 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
87 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
88 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
89 conv 19 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 19 0.003 BF
90 route 81 89 14
91 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
92 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
93 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
94 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
95 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
96 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
97 conv 38 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 38 0.006 BF
98 route 90
99 conv 128 7 x 7 / 1 25 x 25 x 185 -> 25 x 25 x 128 1.450 BF
100 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
101 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
102 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
103 conv 128 7 x 7 / 1 25 x 25 x 128 -> 25 x 25 x 128 1.004 BF
104 conv 128 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 128 0.020 BF
105 conv 19 1 x 1 / 1 25 x 25 x 128 -> 25 x 25 x 19 0.003 BF
106 route 105 97
Note
- Darknet version openpose.cfg and openpose.weight are ported from COCO version
pose_deploy_linevec.prototxt and pose_iter_440000.caffemodel.
- You could change net input width, height in openpose.cfg.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].