YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide - 코드 세계

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

발 2023-09-29 20:50:48 독서 시간: null

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출처blog.csdn.net/m0_47867638/article/details/132528482

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

Raising point artifact: SPD-Conv based on Yolov8, low-resolution images and small objects have obvious rising points

Optimize and improve the YOLOv5 algorithm: add the SPD-Conv module to make small targets have nowhere to hide - (super detailed)

Yolov8 improvement --- attention mechanism: ShuffleAttention, ECA, EffectiveSE, SE

Raising point artifact: SPD-Conv based on Yolov8, low-resolution images and small objects have obvious rising points

yolov8 use

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

YOLOV8 improvement: Add RCS-OSA module to improve detection speed

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

YOLOv8 개선

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

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

YOLOv8 is evaluated using pycocotools

yolov8 training experience is continuously updated

Yolov8 표적 탐지

YOLOv8 model deployment based on AidLux

YOLOv5, YOLOv8 개선-BIFPN

YOLOv5, YOLOv8 개선: BoTNet Transformer

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

Mine electronic seal yolov8 network model

YOLOV8 Onnxruntime Opencv DNN C++ 배포

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