• 中文介绍https://zhuanlan.zhihu.com/p/234506503
• 9.12：更新NCNN Camera Demo https://github.com/dog-qiuqiu/Yolo-Fastest/tree/master/ncnn_sample
• On some GPUs (such as NVIDIA PASCAL: 1080ti, 1070...), Darknet Group convolution is not well supported, which will cause the problem of low training inference efficiency, but it will not appear on the 20 series and 16 series graphics cards, for example The reasoning time for 2080ti is 2ms, and 1660ti is 3ms. It is suspected to be the cause of CUDNN. It is recommended that users in this situation use pytorch for training and inference
• 在一些GPU上（如NVIDIA PASCAL:1080ti，1070…），Darknet分组卷积没有得到很好的支持，这会导致训练/推理效率低下的问题，但在20系列和16系列显卡上不会出现，例如2080ti的推理时间为2ms，1660ti为3ms，可能是CUDNN的原因。建议这种情况下的用户使用pytorch进行训练和推理
• Darknet CPU推理效率优化不好，不建议使用Darknet作为CPU端的推理框架，建议使用NCNN
• Based on pytorch training framework: https://github.com/dog-qiuqiu/yolov3

• Simple, fast, compact, easy to transplant
• A real-time target detection algorithm for all platforms
• The fastest and smallest known universal target detection algorithm based on yolo
• Optimized design for ARM mobile terminal, optimized to support NCNN reasoning framework
• Based on NCNN deployed on RK3399 ,Raspberry Pi 4b... and other embedded devices to achieve full real-time 30fps
• The speed is 45% faster than mobilenetv2-yolov3-nano, and the parameter amount is reduced by 56%

• Test platform Kirin 990 CPU，Based on NCNN
• COCO 2017 Val mAP
• Suitable for hardware with extremely tight computing resources
• This model is recommended to do some simple single object detection suitable for simple application scenarios

• Performance indicators reference from the papers and public indicators in the github project
• MobileNetv2-Yolo-Lite: https://github.com/dog-qiuqiu/MobileNet-Yolo#mobilenetv2-yolov3-litenano-darknet

loop_count = 4
num_threads = 4
powersave = 0
gpu_device = -1
cooling_down = 1
        yolo-fastest  min =   62.58  max =   62.76  avg =   62.70
      squeezenet_ssd  min =  380.98  max =  391.39  avg =  387.53
 squeezenet_ssd_int8  min =  458.05  max =  467.54  avg =  463.12
       mobilenet_ssd  min =  212.31  max =  223.34  avg =  218.93
  mobilenet_ssd_int8  min =  359.98  max =  374.03  avg =  365.17
      mobilenet_yolo  min =  619.65  max =  635.44  avg =  628.29
  mobilenetv2_yolov3  min =  294.92  max =  304.95  avg =  298.43
         yolov4-tiny  min =  855.50  max = 1074.92  avg =  962.78

• This repo is based on Darknet project so the instructions for compiling the project are same (https://github.com/MuhammadAsadJaved/darknet#how-to-compile-on-windows-legacy-way)

Just do make in the Yolo-Fastest-master directory. Before make, you can set such options in the Makefile: link

• GPU=1 to build with CUDA to accelerate by using GPU (CUDA should be in /usr/local/cuda)
• CUDNN=1 to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in /usr/local/cudnn)
• CUDNN_HALF=1 to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x
• OPENCV=1 to build with OpenCV 4.x/3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams
• Set the other options in the Makefile according to your need.

*Run Yolo-Fastest , Yolo-Fastest-xl , Yolov3 or Yolov4 on image or video inputs

*Note: change .data , .cfg , .weights and input image file in image_yolov3.sh for Yolo-Fastest-x1, Yolov3 and Yolov4

  sh image_yolov3.sh

*Note: Use any input video and place in the data folder or use 0 in the video_yolov3.sh for webcam

*Note: change .data , .cfg , .weights and input video file in video_yolov3.sh for Yolo-Fastest-x1, Yolov3 and Yolov4

  sh video_yolov3.sh

  ./darknet partial yolo-fastest.cfg yolo-fastest.weights yolo-fastest.conv.109 109

• 交流qq群:1062122604
• https://github.com/AlexeyAB/darknet

  ./darknet detector train voc.data yolo-fastest.cfg yolo-fastest.conv.109 

• Benchmark:https://github.com/Tencent/ncnn/tree/master/benchmark
• NCNN supports direct conversion of darknet models
• darknet2ncnn: https://github.com/Tencent/ncnn/tree/master/tools/darknet

• https://github.com/dog-qiuqiu/Yolo-Fastest/tree/master/ncnn_sample

• https://github.com/WZTENG/YOLOv5_NCNN

• https://github.com/dog-qiuqiu/MobileNet-Yolo#darknet2caffe-tutorial

• https://github.com/CaoWGG/TensorRT-YOLOv4
• It is not efficient to run on Psacal and earlier GPU architectures. It is not recommended to deploy on such devices such as jeston nano(17ms/img), Tx1, Tx2, but there is no such problem in Turing GPU, such as jetson-Xavier-NX Can run efficiently

• https://blog.csdn.net/nihate/article/details/108670542

• https://github.com/AlexeyAB/darknet
• https://github.com/Tencent/ncnn