Improvements of YOLOv5 over YOLOv4 - Code World

Improvements of YOLOv5 over YOLOv4

Language 2023-08-15 17:56:12 views: null

Compared with YOLOv4, YOLOv5 has greatly improved in small object detection, computing performance and real-time performance. It introduces new improvements such as more efficient model structure, more powerful Adaptive Pyramid Pooling (APo), improved image augmentation and scale-based data augmentation (SDE), and more.

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Origin blog.csdn.net/weixin_35749786/article/details/129501872

Improvements of YOLOv5 over YOLOv4

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.83] Change the backbone feature extraction network Backbone to RevCol

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.79] The improved loss function is VariFocal Loss

YOLOv5, YOLOv8 improvements-BIFPN

YOLOv5, YOLOv8 improvements: BoTNet Transformer

What improvements does YOLOv8 have over the previous generation?

YOLOV5 improvements: replaced by MPDIOU to achieve effective gains

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.70] NonDeepNe (ParNet module), a lightweight attention mechanism that does not increase network depth

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.84] are integrated into the 2023 latest large convolution kernel CNN architecture RepLKNet upgraded version-UniRepLKNet

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.80] introduce plug-and-play CloFormer: the perfect integration of attention mechanism and convolution

Improvements to YOLOv8

Jetson Nano (5) TensorRT yolov4 yolov4-tiny yolov5 actual measurement

YOLOv5 improvements | 2023 main article | FasterNeT running feature extraction network (improves FPS and detection efficiency)

YOLOv5 improvements | Main article | ConvNeXtV2 fully convolutional masked autoencoder network

YOLOv5 improvements | Main article | CSWinTransformer cross-shaped window network improved feature fusion layer

YOLOv5 improvements | 2023 attention | EMAttention attention mechanism (with multiple positions that can be added)

YOLOv4 Structure

yolov4 large

YOLOv4【Continuará】

YOLOv5 improvements | Loss functions | More than 20 loss functions such as EIoU, SIoU, WIoU, DIoU, FocusIoU, etc.

yolov3 yolov4 yolov5 yolov6 yolov7 yolov8 yolox Algorithm improvement, algorithm customization, algorithm guidance

YOLODet's strongest PyTorch version of YOLOv5, YOLOv4, PP-YOLO, YOLOv3 reproduce

deepstream and yolov4 v5

yolov4 training problem

A preliminary study of yolov4

【深度学习】YOLOv4

关于YOLOv4理论

Analysis of yolov4 papers

Target detection: YOLOV4

YOLOv4 study notes

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