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

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

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Origin blog.csdn.net/Wansit/article/details/128684521

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

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.67] Add attention mechanism MHSA

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.71] attention mechanism Expectation-Maximization Attention (EMA module)

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.72] attention mechanism Linear Context Transform Block (LCT module)

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.73] Adding asymptotic feature pyramid network (AFPN module)

Target detection algorithm (R-CNN, fast R-CNN, faster R-CNN, yolo, SSD, yoloV2, yoloV3, yoloV4, yoloV5, yoloV6, yoloV7)

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.85] Use TransformerDecoderHead in RT-DETR to improve yolov5

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.69] Improvement of small targets in remote sensing image target detection CATnet (ContextAggregation module)

[YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement NO.65] CVPR 2023 | Tsinghua team's plug-and-play network architecture—Slide-Transformer

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

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

[YOLOv7/YOLOv5 series algorithm improvement NO.7] Loss function improvement

YOLOv7 algorithm reconstruction: personal self-improvement, as simple as YOLOv5, only 31 layers

Nanny level tutorial - YOLO series (YOLOv5/YOLOv7/YOLOv8) algorithm training data set

[YOLOv7/YOLOv5 series algorithm improvement NO.8] Non-maximum suppression NMS algorithm improvement Soft-nms

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.80] introduce plug-and-play CloFormer: the perfect integration of attention mechanism and convolution

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 use introduction and YOLOv7, YOLOv6, YOLOv5 performance comparison

Interpretation of Yolov5 algorithm

Video target detection yolo algorithm small model self-training model comparison (yolov5 yolov7 yolov8)

YOLOV5/YOLOV7/YOLOV8 Improvement Column

Analysis of YOLOv4 algorithm for target detection

Target detection algorithm Yolov7

Analysis of YOLOv3 algorithm for target detection

Improved module of YOLOv8 algorithm

yolov5 pedestrian detection algorithm

Analysis of YOLOv5 algorithm for target detection

[YOLOv7/YOLOv5 series improvement NO.52] Integrating the C2f module in YOLOv8

[YOLO series] YOLOv5, YOLOX, YOOv6, YOLOv7 network model structure

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