lightnet

lightnet is a turnkey solution to real world problems accelerated with deep learning AI technology, including but not limited to object detection, image classification and human pose estimation.

• The source code
• Build instructions
• End2end Object Detection
• End2end Image Classification
• Human Pose Estimation
• FAQ

How to read the source code

This project is dependent on a few open-source projects:

• modules/darknet - the main engine for training & inferencing.
• modules/Yolo_mark - the toolkit to prepare training data for object detection.
• modules/yolo2_light - lightweighted inferencing engine [optional].
• modules/cvui - lightweighted GUI based purely on OpenCV.
• moudles/pytorch-caffe-darknet-convert - DL framework model converter
• modules/minitrace - library to generate tracing logs for Chrome "about:tracing"
• modules/readerwriterqueue - single-producer, single-consumer lock-free queue for C++
• modules/bhtsne - Barnes-Hut implementation of the t-SNE algorithm

How to build from Visual Studio 2015

Install NVIDIA SDK

• Download CUDA 11.x

• Download cuDNN v8.x

  • Extract to the same folder as CUDA SDK
  • e.g. c:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\

• Download zlib

Install OpenCV

• Latest OpenCV: e.g. https://github.com/opencv/opencv/releases/
• Extract to C:\opencv\

Build it

Execute the batch file

build.bat

Object Detection - inference w/ pre-trained weights

First you need to download the weights. You can read more details on darknet website.

Syntax for object detection

darknet.exe detector demo <data> <cfg> <weights> -c <camera_idx> -i <gpu_idx>
darknet.exe detector demo <data> <cfg> <weights> <video_filename> -i <gpu_idx>
darknet.exe detector test <data> <cfg> <weights> <img_filename> -i <gpu_idx>

Default launch device combination is -i 0 -c 0.

Run yolov4 on camera #0

darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights

Run yolov3 on camera #0

darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights

Run yolo9000 on camera #0

darknet.exe detector demo cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights

Run yolo9000 on images

darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights

Run yolo9000 CPU on camera #0

darknet_no_gpu.exe detector demo cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights

Object Detection - label images manually

• delete all files from directory my-yolo-net/img and put your .jpg-images in
• change numer of classes (objects for detection) in file my-yolo-net/obj.data: https://github.com/jing-vision/yolo-studio/blob/master/networks/yolo-template/obj.data#L1
• put names of objects, one for each line in file my-yolo-net/obj.names: https://github.com/jing-vision/yolo-studio/blob/master/networks/yolo-template/obj.names
• Run file: my-yolo-net/yolo_mark.cmd

Object Detection - train yolo v2 network

0. Fork __template-yolov2 to my-yolo-net

1. Download pre-trained weights for the convolutional layers: http://pjreddie.com/media/files/darknet19_448.conv.23 to bin/darknet19_448.conv.23

2. To training for your custom objects, you should change 2 lines in file obj.cfg:

• change classes in obj.data#L1
• set number of classes (objects) in obj.cfg#L230
• set filter-value equal to (classes + 5)*5 in obj.cfg#L224

3. Run my-yolo-net/train.cmd

Object Detection - train yolo v3 network

0. Fork __template-yolov3 to my-yolo-net

1. Download pre-trained weights for the convolutional layers: http://pjreddie.com/media/files/darknet53.conv.74 to bin/darknet53.conv.74

2. Create file obj.cfg with the same content as in yolov3.cfg (or copy yolov3.cfg to obj.cfg) and:

• change line batch to batch=64
• change line subdivisions to subdivisions=8
• change line classes=80 to your number of objects in each of 3 [yolo]-layers:
  • obj.cfg#L610
  • obj.cfg#L696
  • obj.cfg#L783
• change [filters=255] to filters=(classes + 5)x3 in the 3 [convolutional] before each [yolo] layer
  • obj.cfg#L603
  • obj.cfg#L689
  • obj.cfg#L776

So if classes=1 then should be filters=18. If classes=2 then write filters=21.

(Do not write in the cfg-file: filters=(classes + 5)x3)

(Generally filters depends on the classes, coords and number of masks, i.e. filters=(classes + coords + 1)*<number of mask>, where mask is indices of anchors. If mask is absence, then filters=(classes + coords + 1)*num)

So for example, for 2 objects, your file obj.cfg should differ from yolov3.cfg in such lines in each of 3 [yolo]-layers:

  [convolutional]
  filters=21

  [region]
  classes=2

Image Classification - inference w/ pre-trained weights

Again, you need download weights first. You can read more details on darknet website.

Image Classification - train darknet19_448 network

0. Fork __template-darknet19_448 to my-darknet19-net

1. Download pre-trained weights for the convolutional layers: http://pjreddie.com/media/files/darknet19_448.conv.23 to bin/darknet19_448.conv.23

2. Create file obj.cfg with the same content as in darknet19_448.cfg (or copy darknet19_448.cfg to obj.cfg) and:

• set batch to 128 or 64 or 32 depends on your GPU memory in darknet19-classify.cfg#L4
• change line to subdivisions=4
• set filter-value equal to classes in darknet19-classify.cfg#L189

Human Pose Estimation - inference w/ pre-trained weights

This project lives in DancingGaga

For more details, please check the README there.

[Weight file] (darknet version openpose.weight)

https://drive.google.com/open?id=1BfY0Hx2d2nm3I4JFh0W1cK2aHD1FSGea

FAQ

blobFromImage() vs letterbox_image() vs resize_image()

https://github.com/AlexeyAB/darknet/issues/232#issuecomment-336955485

How to fix CUDA Error: no kernel image is available for execution on the device?

# Tesla V100
# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]

# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores
# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75]

# Jetson XAVIER
# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72]

# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4
# ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61

# GP100/Tesla P100 - DGX-1
# ARCH= -gencode arch=compute_60,code=sm_60

# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment:
# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53]

# For Jetson Tx2 or Drive-PX2 uncomment:
# ARCH= -gencode arch=compute_62,code=[sm_62,compute_62]

How to fine tune a existing network?

https://github.com/pjreddie/darknet/wiki/YOLO:-Real-Time-Object-Detection

darknet.exe partial cfg/darknet.cfg darknet.weights darknet.conv.13 13 darknet.exe partial cfg/extraction.cfg extraction.weights extraction.conv.23 23 darknet.exe partial cfg/darknet19.cfg darknet19.weights darknet19.conv.23 23 darknet.exe partial cfg/darknet19_448.cfg darknet19_448.weights darknet19_448.conv.23 23 darknet.exe partial cfg/darknet53.cfg darknet53.weights darknet53.conv.74 74 darknet.exe partial cfg/resnet50.cfg resnet50.weights resnet50.conv.66 66

Explanation of yolo training output

https://github.com/rafaelpadilla/darknet#faq_yolo

CFG Parameters

• in net section
• in layer section

How to use scripts folder?

pip install -r scripts/requirements.txt