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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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

[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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