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

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

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

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

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.67] Add attention mechanism MHSA

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

YOLOv5 Improvement Series (17)——Replacing MPDIoU of IoU (ELSEVIER 2023|Beyond WIoU, EIoU, etc.|Measured increase point)

Gain point artifact: small target performance improvement based on Yolov5/Yolov7

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

YOLOv5 improvement series (18) - replace Neck's AFPN (new progressive feature pyramid | beyond PAFPN | measured increase point)

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

The first release of Yolov5/Yolov7 point increase artifact: Huawei Noah 2023's minimalist neural network model VanillaNet---VanillaBlock helps detection and achieves violent point increase

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

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

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

YOLOv5 Improvement Series (23) - MobileViTv2 to replace the backbone network (an efficient separable self-attention mechanism for mobile vision Transformer)

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

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

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

pytorch code implements Biformer of attention mechanism

YOLOv5 improvement series (11) - add loss function EIoU, AlphaIoU, SIoU, WIoU

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

YOLOv8 개선 | 플러그 앤 플레이 | CVPR2023 최신 관심 | "BiFormer: Visual Transformer 및 Double-Layer Routing Attention"

YOLOv5 improvements | 2023 attention | EMAttention attention mechanism (with multiple positions that can be added)

YOLOV5/YOLOV8 improvement: CVPR23: join the ConvNeXt V2 backbone, effectively increase points

CVPR2023 Plug and Play Series | An Efficient and Lightweight Self-Attention Mechanism Helps Image Restoration Network Win SOTA!

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

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)

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

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

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