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

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

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Origin blog.csdn.net/m0_70388905/article/details/128661165

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

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

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

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

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

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.69] Improvement of small targets in remote sensing image target detection CATnet (ContextAggregation module)

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

Detailed explanation of c2f module of YOLOv8

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

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

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

yolov7 improvement series

YOLOV5/YOLOV7/YOLOV8 Improvement Column

[Amélioration de la série YOLOv7/YOLOv5 NO.52] Intégration du module C2f dans YOLOv8

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

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.70] NonDeepNe (ParNet module), a lightweight attention mechanism that does not increase network depth

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

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

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

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

C2f layer in yolov8

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

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 improvements [NO.83] Change the backbone feature extraction network Backbone to RevCol

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

[Mejora de la serie YOLOv7/YOLOv5 NO.52] Integración del módulo C2f en YOLOv8

[Melhoria da série YOLOv7/YOLOv5 NO.52] Integrando o módulo C2f no YOLOv8

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

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