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

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

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Origin blog.csdn.net/m0_51530640/article/details/131412297

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

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

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

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

YOLOv5 Improvement Series (20) - Add BiFormer Attention Mechanism (CVPR2023|Small Goal Increase Point Artifact)

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

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

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

Interpretation of the new version of Yolov8: How to add optimization points to the new version, and compare and explain by adding EMA attention

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

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

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

YOLOV7 adds CBAM attention mechanism in different positions

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)

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

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

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

YOLOv5 Improvement | 2023 Attention Chapter | iRMB Inverted Residual Block Attention Mechanism (Lightweight Attention Mechanism)

YOLOV8 improvement: add Wiseiou, effectively increase points

Detailed explanation of c2f module of YOLOv8

Yolov5/Yolov7 improvement---attention mechanism: ICASSP2023 EMA is based on efficient multi-scale attention of cross-space learning, the effect is better than ECA, CBAM, CA | small target rises significantly

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

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

YoloV8 improvement --- attention mechanism: the introduction of GCNet, GENet, the effect is better than SENet

Yolov8 improvement --- attention mechanism: CoordAttention, the effect spikes CBAM, SE

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

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

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

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

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

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