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

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

News 2023-07-25 18:55:28 views: null

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Origin blog.csdn.net/m0_47867638/article/details/131546993

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

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

YoloV8 improvement strategy: deformable large-core attention D-LKA, YoloV8’s super-large sauce latte

YoloV8 improvement strategy: lightweight Slim Neck creates the ultimate YoloV8

YoloV8 improvement strategy: BAM bottleneck attention module | BAM detailed explanation and code comments | CBAM companion article | Effective points

YoloV8 improvement strategy: FastVit and YoloV8 are perfectly integrated, and the YoloV8 network is reconstructed with heavy parameters (first release on the entire network)

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

YoloV8 improvement strategy: Diverse Branch Block improves YoloV8 and continues to fight against wolves on the heavy parameter structure.

Improve YOLOv8 | Backbone Network | YOLOv8 replaces the SwinTransformer of the backbone network | "Hierarchical Visual Transformer Based on Displacement Window"

YoloV8 improvement strategy: CoordConv adds coordinates to the convolution, making it spatially aware.

YoloV8 improvement strategy: Shape-IoU, considering the measurement of border shape and scale

YoloV8 improvement --- attention mechanism: the introduction of GCNet, GENet, the effect is better than SENet

Yolov8 improvement --- attention mechanism: CoordAttention, the effect spikes CBAM, SE

YoloV8 improvement strategy: Intel parameter-oriented efficient dynamic convolution KernelWarehouse, YoloV8’s top-notch package

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.67] Add attention mechanism MHSA

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.72] attention mechanism Linear Context Transform Block (LCT module)

Optimization + quantization, let your YOLOv8 get 1000+ FPS performance

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

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

YOLOv8 model deployment based on AidLux

Attention mechanism: based on Yolov8's Triplet attention module, plug and play, the effect is better than cbam and se, and the increase point is obvious

[Yolov8] Deploy a full range of Yolov8 models based on C# and TensorRT

[Yolov8] Deploy a full range of Yolov8 models based on C# and OpenVINO

YOLOV5/YOLOV7/YOLOV8 Improvement Column

YOLOv8 use introduction and YOLOv7, YOLOv6, YOLOv5 performance comparison

YOLOv8’s latest improvement series: Optimize CIoU to Wise-IoU to help improve detection performance and effectively improve small target detection accuracy! ! ! Far ahead! ! ! ! !

Field smoke detection based on Yolov8 (1)

Python implements object detection based on Yolov8 + CPU

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