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

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

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Origin blog.csdn.net/weixin_43694096/article/details/130624384

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

Improve YOLOv7 | Add [CA] [ECA] [CBAM] [SE] attention mechanism in ELAN module and ELAN-H module | Attach detailed structure diagram

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

Improve YOLOv7 | Add [Triplet] [SpatialGroupEnhance] [NAM] [S2] attention mechanism to the ELAN module | Attached detailed structure diagram

yolov5 added CBAM, SE, CA, ECA attention mechanism, pure code (22.3.1 also updated)

YOLOv5-6.1 adds attention mechanism (SE, CBAM, ECA, CA)

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

Interpretation of Pytorch image processing attention mechanism SENet CBAM ECA module

Attention module SE, CBAM, ECA three code implementations

Plug and Play Lightweight Attention Mechanism ECA--Net

Detailed explanation of c2f module of YOLOv8

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

Yolov8 improves the CoTAttention attention mechanism, and the effect instantly kills CBAM and SE

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

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

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

Attention mechanism - Convolutional Block Attention Module (CBAM)

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

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

[Attention] CBAM mechanism CV in companion -BAM module

Detailed Interpretation of Vernacular (8) ----- CBAM: Convolutional Block Attention Module

Detailed Attention Mechanism in Images (SEBlock | ECABlock | CBAM)

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

ECA attention mechanism

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

Optimize and improve the receptive field attention convolution operation (RFAConv) of the YOLOv5 algorithm, and the effect is faster than CBAM and CA, etc.

Attention mechanism CBAM implementation

Yolov5/Yolov7 improvement --- attention mechanism: Polarized Self-Attention, the effect spikes CBAM, SE

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

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