DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth! - Code World

DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth!

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DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth!

DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth!

DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth!

DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth!

DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth!

DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth!

DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth!

Improved YOLOv8 | Plug and Play | Full-Dimensional Dynamic Convolution | "OMNI-DIMENSIONAL DYNAMIC CONVOLUTION"

YOLOv5, v7 improvement twenty-eight: ICLR 2022 rising point artifact - plug-and-play dynamic convolution ODConv

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

YOLOv7 Improvement: CVPR 2023 | SCConv: Plug-and-Play Convolution for Spatial and Channel Reconstruction

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

Artifact with rising points: CVPR2023 InceptionNeXt. When Inception meets ConvNeXt, plug and play in Yolov5/Yolov7/Yolov8, small target detection has obvious points.

Photovoltaic cell defect detection based on Yolov8 introduces ICCV2023 dynamic snake convolution and the exclusive network-first multi-dimensional collaborative attention module MCA, achieving full innovation and growth

CFPNet based on YoloV5---ECVBlock's small target detection, plug and play, helping to detect rising points

Practical implementation of industrial small target defect detection based on Yolov8 (2): Dynamic Snake Convolution (Dynamic Snake Convolution), achieving violent gains | ICCV2023

Gain point artifact: small target performance improvement based on Yolov5/Yolov7

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

Attention mechanism: based on Yolov8's Triplet attention module, plug and play, the effect is better than cbam and se, and the increase point is obvious

Data enhancement: Based on Yolov5/Yolov7/Yolov8---automatically generate pictures and xml files to solve problems such as difficult training of small samples

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

YOLOv5/YOLOv8 verbesserte praktische Experimente: Original neue *** Subpixel-Faltung *** optimierte Upsampling-Technologie zur Verbesserung des Zielerkennungseffekts (Plug and Play)

Yolov5/Yolov7 improvement --- attention mechanism: SimAM (no parameter Attention) and NAM (based on standardized attention module), the effect spikes CBAM, SE

Interpretation of paper "Plug-and-Play Priors for Model Based Reconstruction"

Улучшение YOLOV8: CVPR 2023 | SCConv: свертка пространственной и канальной реконструкции Plug-and-Play

YoloV5 improvement strategy: AAAI 2024 latest axial attention | Plug and play, the first choice for improvement | First launched on the entire network, including data sets and codes, ready to use out of the box!

Dynamic growth of array space

DSCNet: Dynamic serpentine convolution tubular structure segmentation based on topological geometric constraints

YOLOv8 improves the effective point increase series->Teach you step-by-step to add dynamic snake convolution (Dynamic Snake Convolution)

Automatic annotation based on yolov5

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