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

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

Enterprise 2023-06-25 12:35:16 views: null

Table of contents

1. SimAM: No parameter Attention

1.1 Join yolov5 common.py

1.2 Join yolo.py:

1.3 yolov5s_SimAM.yaml

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Origin blog.csdn.net/m0_63774211/article/details/131373186

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

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

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

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

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

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

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

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

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

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

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

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

Attention mechanism - Convolutional Block Attention Module (CBAM)

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

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

YOLOV7 adds CBAM attention mechanism in different positions

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

[Attention] CBAM mechanism CV in companion -BAM module

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

Attention mechanism CBAM implementation

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

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

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

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

CBAM attention mechanism - pytorch implementation

pytorch code implements SimAM of attention mechanism

CBAM: Convolutional Block Attention Module—— channel attention + spatial attention

Interpretation of Pytorch image processing attention mechanism SENet CBAM ECA module

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

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