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

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

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

YoloV7 improvement strategy: Frequency domain multi-axis representation learning module | First release on the entire network | High-efficiency increase points | Detailed code comments

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

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

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

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

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

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!

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

Improving YOLOv5/YOLOv8: Reproduction combined with plug-and-play | Efficient multi-scale attention (EMA), the module becomes a small helper for YOLOv5 improvement

The first release of Yolov5/Yolov7 point increase artifact: Huawei Noah 2023's minimalist neural network model VanillaNet---VanillaBlock helps detection and achieves violent point increase

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

First release on the entire network! Hongmeng Application Development Learning Roadmap Standard Edition!

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

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

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

YOLOv8 improvements | Main article | The latest results in December TransNeXt feature extraction network (first release on the entire network)

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

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.73] Adding asymptotic feature pyramid network (AFPN module)

Learning in the Frequency Domain 解读

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

Detailed explanation and code implementation of each module of YOLOv5 Focus C3

Realization of ReID code based on representation learning (5)

[MATLAB Issue 70] LightGbm (LGBM) gradient boosted decision tree multi-input single-output regression prediction and multi-classification prediction model based on MATLAB (first release on the entire network)

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

Detailed explanation of yolov5 detection code

Yolov5 network component code

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

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.70] NonDeepNe (ParNet module), a lightweight attention mechanism that does not increase network depth

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