Reproduce the latest MPDIoU improvement of YOLOv5: effective and accurate bounding box regression loss, defeating G/E/CIoU, the effect is obvious! ! ! - Code World

Reproduce the latest MPDIoU improvement of YOLOv5: effective and accurate bounding box regression loss, defeating G/E/CIoU, the effect is obvious! ! !

Enterprise 2023-09-12 06:35:34 views: null

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Origin blog.csdn.net/qq_44224801/article/details/131966863

Reproduce the latest MPDIoU improvement of YOLOv5: effective and accurate bounding box regression loss, defeating G/E/CIoU, the effect is obvious! ! !

Reproduce the latest MPDIoU improvement of YOLOv8: effective and accurate bounding box regression loss, defeating G/E/CIoU, the effect is obvious! ! !

Reproduce the latest MPDIoU improvement of YOLOv8: effective and accurate bounding box regression loss, defeating G/E/CIoU, the effect is obvious! ! !

The Magic of Bounding Box Regression: Demystifying the Accurate and Efficient MPDIoU Loss Function

MPDIoU: A Loss for Efficient and Accurate Bounding BoxRegression

YOLOv5 comprehensive analysis tutorial ③: Faster and better bounding box regression loss

MPDIoU: A Loss for Efficient and Accurate Bounding BoxRegression--Paper Study Notes

Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism

Bounding-Box Regression

[Target Detection] AAAI20 - Improving Bounding Box Regression "Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression"

Bounding Box Regression Strategies in OSTrack

YOLOV5 improvements: replaced by MPDIOU to achieve effective gains

[Reserved] border regression (Bounding Box Regression)

Practical improvement of YoloV5: using MPDIoU to improve YoloV5

YoloV5 improvement strategy: AAAI 2024 latest axial attention | Plug and play, the first choice for improvement | First launched on the entire network, including data sets and codes, ready to use out of the box!

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

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

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

YOLOV7 improvements: replaced by MPDIOU to achieve effective gains

yolo increases MPDIoU loss

Unity Bounds bounding box (bounding box)

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

Remove bounding box outline

[Target detection algorithm] IOU, GIOU, DIOU, CIOU and YOLOv5 loss function

Воспроизведите последнее улучшение MPDIoU для YOLOv5: эффективную и точную потерю регрессии ограничивающей рамки, победу над G/E/CIoU, эффект очевиден! ! !

Воспроизведите последнее улучшение MPDIoU для YOLOv5: эффективную и точную потерю регрессии ограничивающей рамки, победу над G/E/CIoU, эффект очевиден! ! !

YOLOv5 Improvement Series (21) - RepViT to replace the backbone network (Tsinghua ICCV 2023|The latest open source mobile terminal ViT)

The latest loss function SIOU--adapted to YOLOv5 version 6.0 code implementation, it can indeed be increased after testing

Reproduzieren Sie die neueste MPDIoU-Verbesserung von YOLOv8: effektiver und genauer Bounding-Box-Regressionsverlust, Besiegen von G/E/CIoU, der Effekt ist offensichtlich! ! !

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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