YOLOV5/YOLOV7/YOLOV8 Improvement Column - Code World

YOLOV5/YOLOV7/YOLOV8 Improvement Column

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Origin blog.csdn.net/m0_51530640/article/details/131529309

YOLOV5/YOLOV7/YOLOV8 Improvement Column

[YOLOv7/YOLOv5 series improvement NO.52] Integrating the C2f module in YOLOv8

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

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.71] attention mechanism Expectation-Maximization Attention (EMA module)

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.72] attention mechanism Linear Context Transform Block (LCT 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)

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

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

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

YOLOv7 algorithm reconstruction: personal self-improvement, as simple as YOLOv5, only 31 layers

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

yolov7 improvement series

[YOLOv7/YOLOv5 series algorithm improvement NO.8] Non-maximum suppression NMS algorithm improvement Soft-nms

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

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

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

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

Detailed explanation of YOLOv7 improvement

YoloV8 improvement strategy: NWD new paradigm of small target detection, helping YoloV5 and V8 to violently increase their points on small targets

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

YOLOv8 use introduction and YOLOv7, YOLOv6, YOLOv5 performance comparison

YOLOV7 improvement: replace WIOU to achieve lossless gains

YoloV8 improvement strategy: RepViT improves YoloV8, lightweight Block helps YoloV8 achieve better mobility

YoloV8 improvement strategy: Based on hierarchical attention FasterViT, let YoloV8 achieve a leap in performance

YoloV8 improvement strategy: lightweight Slim Neck creates the ultimate YoloV8

Model test comparison and conclusion of YOLOv5 YOLOv7 YOLOv8

Practical improvement of YoloV5: using MPDIoU to improve YoloV5

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