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

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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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 improvements [NO.70] NonDeepNe (ParNet module), a lightweight attention mechanism that does not increase network depth

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.67] Add attention mechanism MHSA

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

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.84] are integrated into the 2023 latest large convolution kernel CNN architecture RepLKNet upgraded version-UniRepLKNet

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.83] Change the backbone feature extraction network Backbone to RevCol

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.79] The improved loss function is VariFocal Loss

[YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement NO.65] CVPR 2023 | Tsinghua team's plug-and-play network architecture—Slide-Transformer

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

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

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.85] Use TransformerDecoderHead in RT-DETR to improve yolov5

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

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

YOLOV5 adds attention mechanism, taking ECAttention as an example (series)

YOLOv7 Improvement: CVPR 2023 | SCConv: Plug-and-Play Convolution for Spatial and Channel Reconstruction

Nanny level tutorial - YOLO series (YOLOv5/YOLOv7/YOLOv8) algorithm training data set

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, Decoder and Encoder Improvements

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

yolov3 yolov4 yolov5 yolov6 yolov7 yolov8 yolox Algorithm improvement, algorithm customization, algorithm guidance

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

DRConv dynamic region-aware convolution based on Yolov5/Yolov7, plug and play, with significant growth!

YOLOv5, v7 improvement twenty-eight: ICLR 2022 rising point artifact - plug-and-play dynamic convolution ODConv

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

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

[YOLOv7/YOLOv5 series algorithm improvement NO.7] Loss function improvement

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

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

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