YOLOv4

• YOLOv4: https://arxiv.org/abs/2004.10934

• Scaled-YOLOv4: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html

• YOLOR: https://arxiv.org/abs/2105.04206

YOLOv4 pre-release

YOLOv4

YOLOv4 consists of: https://arxiv.org/abs/2004.10934

• Backbone: CSPDarknet53
• Neck: SPP, PAN
• Head: YOLOv3

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Download:

• cfg-file: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.cfg

• weights-file: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights

• pre-trained weights-file for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137

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Accuracy (AP) / Speed (FPS):

YOLOv4 (416x416 batch=1 FP16) - 32 FPS on Jetson Xavier AGX by using tkDNN+TensorRT: https://github.com/ceccocats/tkDNN

Size	Darknet FPS (avg)	tkDNN TensorRT FP32 FPS	tkDNN TensorRT FP16 FPS	tkDNN TensorRT FP16 batch=4 FPS	Speedup
320	100.6	116	202	423	4.2x
416	82.5	103	162	284	3.5x
512	69.7	91	134	206	2.9x
680	53.6	62	100	150	2.8x

Yolo v3 optimal

Features:

• fusion blocks: FPN, PAN, ASFF, BiFPN
• network modules: ResNet, CPS, SPP, RFB
• network architecture search: CSPResNext50, CSPDarknet53, SpineNet49, EfficientNetB0, MixNet-M
• activations: SWISH, MISH
• other features: weighted-[shortcut], Sigmoid scaling (Scale-sensitivity), Label smoothing, Optimal hyper parameters, Dynamic mini batch size for random shapes, Squeeze-and-excitation, Grouped convolution, MixConv (grouped [route]), Elastic-module
• data augmentation: MixUp, CutMix, Mosaic
• losses: MSE, GIoU, CIoU, DIoU
• detection layers: [yolo] (fixed iou_thresh), [Gaussian_yolo]
• detection on video (sequence of frames) - layers: [crnn] (convolutional-RNN), [conv_lstm] (Convolutional LSTM)

Darknet for Windows & Linux:

Darknet for Windows & Linux:

• object detection: Yolo v3, Yolo v2
• classification: ResNet, Darknet, DenseNet
• sequence prediction RNN-layers: rnn, crnn, gru, lstm
• Tensor Cores are used

Tested for Training and Prediction.

• There are attached compiled binary files of Darknet for Windows x64 (559 MB): https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3/darknet.zip

  Are used: MSVS 2015, CUDA 10.0, cuDNN 7.4.2, OpenCV 3.4.0, GPU CC 3.0 & 7.5
  May be required vc_redist.x64.exe file 13.9 MB (Visual C++ Redistributable for MSVS2015 ): https://www.microsoft.com/en-us/download/confirmation.aspx?id=48145&6B49FDFB-8E5B-4B07-BC31-15695C5A2143=1

Tested Yolo v3 for training and detection on Windows and Linux

Can be used for training and detection Yolo v3 and v2 on Windows and Linux.
Added several performance improvements.
It supports mixed-precision training/detection using Tensor Cores on GPU Volta - set CUDNN_HALF=1 in the Makefile

Tested Yolo v2 for training and detection on Windows and Linux

Yolo_v2_tested

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