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

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

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

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

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

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

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

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

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

YOLOV5 improvement: CVPR 2023 | Add EMA attention mechanism in different positions of C3 block, effectively increase points

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 the RepGhostBottleneck module in the C2f module, effectively increasing points

YOLOV5 improvement: CVPR2023: join the EfficientViT backbone: memory access efficient ViT with cascade group attention

[Lightweight] Practical improvement of YOLOv8 | Replacing the backbone network Backbone with GhostnetV2

Artifact with rising points: CVPR2023 InceptionNeXt. When Inception meets ConvNeXt, plug and play in Yolov5/Yolov7/Yolov8, small target detection has obvious points.

Improved YOLOv5: MobileNEtv2, v3 articles **Must increase points | Innovative and practical | Unified lightweight backbone network, including FastNet, ShuffleNet, mobileNet, Ghostnet, etc.

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

YOLOv5 Improvement Series (23) - MobileViTv2 to replace the backbone network (an efficient separable self-attention mechanism for mobile vision Transformer)

YOLOV8 improvement: add Wiseiou, effectively increase points

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

Practical improvement of YOLOv8 | Replacing the backbone network Backbone (5) Lightweight 2023 latest network VanillaNet, the power of minimalism in deep learning

[Lightweight] Practical improvement of YOLOv8 | Replacing the backbone network Backbone's Ghostnet

Practical improvement of YOLOv8 | Replacement of the backbone network Backbone (6) Lightweight model VanillaNet advanced chapter

YOLOv5 Improvement Series (20) - Add BiFormer Attention Mechanism (CVPR2023|Small Goal Increase Point Artifact)

Improve YOLOv8 | Backbone Network | YOLOv8 Replaces ShuffleNetv2 of the Backbone Network | "ShuffleNet v2: A Practical Guide to Efficient Convolutional Neural Network Architecture Design"

YoloV5 improvement strategy: Frequency domain multi-axis representation learning module | First release on the entire network | Efficient increase points | Detailed code comments

Practical improvement of YOLOv8 | Replacing the backbone network Backbone with the 2023 latest model LSKNet, rotating target detection SOTA

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

YOLOV5/YOLOV7/YOLOV8 Improvement Column

YOLOv8 latest improvement series: YOLOV8 backbone improvements - Huawei Noah proposes a new backbone architecture VanillaNet. YOLOv8 integrates the power of deep learning minimalism to greatly improve the robustness of the model! !

YOLOV8 improvement: CVPR2023: Join the EfficientViT backbone: memory efficient ViT with cascade group attention

YOLOv5 Improvement Series (19) - Swin TransformerV1 to replace the backbone network (ViT model with smaller parameters)

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