Yolov8 lightweight network: Ghostnet, G_ghost, Ghostnetv2 family battle (3): Huawei GhostNet is upgraded again, the best minimalist AI network G_ghost on the whole series of hardware | IJCV22

Enterprise 2023-06-21 08:30:31 views: null

1.Ghostnet, G_ghost, Ghostnetv2 performance comparison

Introduced to yolov8, Bottleneck is combined with c2f to replace all c2f in the backbone.

	layers	parameters	GFLOPs	kb
YOLOv8s	168	11125971	28.4	21991<

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Origin blog.csdn.net/m0_63774211/article/details/131301450

Yolov8 lightweight network: Ghostnet, G_ghost, Ghostnetv2 family battle (3): Huawei GhostNet is upgraded again, the best minimalist AI network G_ghost on the whole series of hardware | IJCV22

Yolov5 lightweight network: Ghostnet, G_ghost, Ghostnetv2 family battle (2): Huawei Ghostnetv2, new SOTA for end-side small model performance | NeurIPS22 Spotlight

Yolov8 軽量ネットワーク: Ghostnet、G_ghost、Ghostnetv2 ファミリーの戦い (3): Huawei GhostNet が再びアップグレードされ、ハードウェアシリーズ全体で最高のミニマリスト AI ネットワーク G_ghost | IJCV22

Improved YOLOv8: combined with lightweight backbone, Huawei's top publication IJCV2022: G-GhostNet!

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

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

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

Improve YOLOv8 | Backbone Network | YOLOv8 replaces GhostNet of the backbone network | Get more features from cheap operations

Interpretation of the new version of Yolov8 (2): How to add the optimization point to the new version, and explain by adding the lightweight network Ghostnetv2 comparison

A fine-grained identification and analysis system for 200 species of birds developed and built based on the lightweight neural network GhostNet

Develop and build a photovoltaic solar cell defect image recognition and analysis system based on the lightweight neural network GhostNet

Yolov8 Lightweight: RepGhost, a hardware-efficient Ghost module through reparameterization

The most easy-to-understand interpretation of the GHostNet network [do not accept refutation]

Improved YOLOv8: combined with lightweight backbone, Huawei's top publication IJCV2022: G-GhostNet!

Improved YOLOv8: combined with lightweight backbone, Huawei's top publication IJCV2022: G-GhostNet!

Improved YOLOv8: combined with lightweight backbone, Huawei's top publication IJCV2022: G-GhostNet!

【Lightweight Network】MobileNet Series

The first launch of Yolov8's point-up artifact: Huawei Noah 2023's minimalist neural network model VanillaNet---VanillaBlock helps detection and achieves violent point-up

YOLOv5 improvement series (24) - MobileViTv3 that replaces the backbone network (a further upgrade of the mobile lightweight network)

YOLOv8 aprimorado: combinado com backbone leve, a principal publicação IJCV2022 da Huawei: G-GhostNet!

Improve YOLOv8 | Backbone Network | YOLOv8 replaces PP-LCNet of the backbone network | "PP-LCNet: A Lightweight CPU Convolutional Neural Network"

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

MySQL is probably the best of the important points of the whole network

Matlab drawing (the best quality article on the whole network)

2023 The best Python automation test in the whole network

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

Improve YOLOv8 | Backbone Network | YOLOv8 replaces MobileNetV3 of the backbone network | "Search for MobileNetV3"

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

La conception légère du backbone YOLOv5 réduit la quantité de calcul en se combinant avec la structure GHostNetv2

【Target Detection】C-GhostNet

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