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

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

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Origin blog.csdn.net/m0_47867638/article/details/135432037

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

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

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

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!

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

Detailed explanation of YOLOv7 improvement

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

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

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

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

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

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

[STL source code analysis] Is list::sort really easy to use? Centos7-Linux environment g++Release vector array sorting and list sorting efficiency test [super detailed comments and explanations]

Learning in the Frequency Domain 解读

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

[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

YOLOV7 improvement: Add RCS-OSA module to improve detection speed

ChatGPT Plus users exclusive: 86 high-efficiency function plug-ins, detailed installation and use of the whole strategy

yolov7 improvement series

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

YOLOv7 Backbone | Detailed explanation of the original source code

The entire network's exclusive first zero-code limit packaging interface automation testing framework full set of tutorials, directly into Alibyte after learning

The latest release on the entire network: Benchmarking Ali P8 Systematic Java Learning Manual! GitHub has exploded

YOLOV8 improvement: add the RepGhostBottleneck module in the C2f module, effectively increasing points

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