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

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

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

yolov7 improvement series

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

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

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

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

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

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

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

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

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

YOLOV5/YOLOV7/YOLOV8 Improvement Column

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

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

Detailed explanation of YOLOv7 improvement

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

Yolov8 loss function improvement: Wasserstein Distance Loss, helping small targets to increase points

YOLOv5 improvement series (13) - replace the activation function of SiLU, ReLU, ELU, Hardswish, Mish, Softplus, AconC series, etc.

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

YOLOV7 improvement: replace WIOU to achieve lossless gains

yolov7 should enter the loss function

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.83] Change the backbone feature extraction network Backbone to RevCol

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

YOLOv7 Improvement 22: Raising Point Artifact - Introducing Recursive Gated Convolution (gnConv)

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