YOLOV8 improvement: add the RepGhostBottleneck module in the C2f module, effectively increasing points - Code World

YOLOV8 improvement: add the RepGhostBottleneck module in the C2f module, effectively increasing points

Enterprise 2023-07-22 20:06:13 views: null

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Origin blog.csdn.net/m0_51530640/article/details/131634009

YOLOV8 improvement: add the RepGhostBottleneck module in the C2f module, effectively increasing points

YOLOV8 improvement: CVPR 2023 | Add EMA attention mechanism in different positions of C2f module, effectively increase points

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

Detailed explanation of c2f module of YOLOv8

Improve YOLOv8 | Plug and Play | Add [SE] [CBAM] [ECA] [CA] attention mechanism in the C2F module | Attach a detailed structure diagram

Improve YOLOv8 | Plug and Play | Add [SimAM] [CoTAttention] [SKAttention] [Double Attention] attention mechanism in the C2F module | Attach a detailed structure diagram

YOLOV8 improvement: Add RCS-OSA module to improve detection speed

YOLOV5/YOLOV8 improvement: CVPR23: join the ConvNeXt V2 backbone, effectively increase points

YoloV8 improvement strategy: BAM bottleneck attention module | BAM detailed explanation and code comments | CBAM companion article | Effective points

YOLOV5 improvement: CVPR 2023 | Add EMA attention mechanism in different positions of C3 block, effectively increase points

Inference on yolov8 on c++ using openvino module

YOLOV7 improvement: Add RCS-OSA module to improve detection speed

YOLOV5 improvement: Add RCS-OSA module to improve detection speed

YOLOV8 improvement: add Wiseiou, effectively increase points

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)

Improved module of YOLOv8 algorithm

YOLOV5 improvement: Add D-LKA Attention at different locations in the C3 module (with the ability to have both SA attention and large convolution kernels)

Improving YOLOv5/YOLOv8: Reproduction combined with plug-and-play | Efficient multi-scale attention (EMA), the module becomes a small helper for YOLOv5 improvement

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

C2f layer in yolov8

YoloV5 improvement strategy: Frequency domain multi-axis representation learning module | First release on the entire network | Efficient increase points | Detailed code comments

YoloV7 improvement strategy: Frequency domain multi-axis representation learning module | First release on the entire network | High-efficiency increase points | Detailed code comments

Yolov8 loss function improvement: Wasserstein Distance Loss, helping small targets to increase points

Improvement of Detection and Segmentation Algorithm (Part 2) Residual Feature Augmentation (RFA Module) and Adaptive Spatial Fusion (ASF Module)

Optimize and improve the YOLOv5 algorithm by adding the DCNv3 module to effectively improve the target detection effect

Increasing the encoding rules to a specific type of module

CSDN add custom module

Add courses module

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