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

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Efficient multi-scale attention (EMA)

Paper introduction
How to join yolov5
- yolo registration
- yaml file
How to join yolov8
- EMA code and how to join
- yaml file 1

EMA attention paper https://arxiv.org/ftp/arxiv/papers/2305/2305.13563.pdf

Paper introduction

Significant Effectiveness of Channel or Space The significant effect of attention mechanisms on producing more discriminable feature representations is illustrated in various computer vision tasks. Visual tasks. However, using channel dimensionality reduction to model cross-channel relationships may bring side effects to extracting deep visual representations. Brings side effects in extracting deep visual representations.
propose

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Origin blog.csdn.net/qq_44224801/article/details/131264759

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

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

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)

Improving the YOLOv5 series: Combining CVPR2021: Multi-head attention Efficient Multi-Head Self-Attention

EMA: An Efficient Multi-Scale Attention Module Based on Cross-Spatial Learning

Amélioration de YOLOv5/YOLOv8 : reproduction combinée avec plug-and-play | Attention multi-échelle efficace (EMA), le module devient une petite aide pour l'amélioration de YOLOv5

Verbesserung von YOLOv5/YOLOv8: Reproduktion kombiniert mit Plug-and-Play | Efficient Multi-Scale Attention (EMA), das Modul wird zu einem kleinen Helfer für die YOLOv5-Verbesserung

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

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

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

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

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

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

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

[YOLOv7/YOLOv5 series improvement NO.53] Integrating the ECVBlock module in the CFPNet network to improve the detection ability of small targets

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

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

YOLOV5 improvement: CVPR2023: join the EfficientViT backbone: memory access efficient ViT with cascade group attention

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

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

Introduction of Yolov5 small target performance improvement solution

YoloV8 improvement strategy: NWD new paradigm of small target detection, helping YoloV5 and V8 to violently increase their points on small targets

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!

YOLOV8 improvements: TripletAttention | plug-and-play lightweight attention mechanism

YOLOV5/YOLOV7/YOLOV8 Improvement Column

Yolov5/Yolov7 improvement --- attention mechanism: ShuffleAttention, ECA, EffectiveSE, SE

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

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)

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