GhostNet: More Features From Cheap Operations
Open accessAbstract
Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https://github.com/huawei-noah/ghostnet.
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The results for ResNet50-GHOST match well with the results reported by Han et al. (2020).
“Our results for the ResNet50-GHOST model, match well with the results reported by Han et al. (2020).”
From Benchmarking and scaling of deep learning models for land cover image classification (2023) · cites this paper as Han et al. (2020)
The GhostConv module reduces computational and parametric counts while maintaining the expressive power of the model.
“The GhostConv module is a lightweight convolutional operation proposed in GhostNet [16], which aims to reduce the computational and parametric counts while maintaining the expressive power of the model.”
From Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning (2024) · cites this paper as [16]
GhostNet uses a plug-and-play Ghost module to divide the original convolutional layer into two parts.
“In 2020, the team proposed a new endto-side neural network architecture called GhostNet [124], which uses a plug-and-play Ghost module to divide the original convolutional layer into two parts.”
From A survey of model compression strategies for object detection (2023) · cites this paper as [124]
The combination of GhostNet[124] and ResNet can achieve excellent model compression effects and build a multi-channel plug-and-play lightweight network structure.
“It can be seen that the combination of GhostNet[124] and ResNet can achieve excellent model compression effects and build a multi-channel plug-and-play lightweight network structure.”
From A survey of model compression strategies for object detection (2023) · cites this paper as [124]
Ghostblock follows the essence of GhostNet and ensures feature diversity while efficiently extracting features.
“On the one hand, Ghostblock follows the essence of GhostNet.It first uses conventional convolution to generate the original feature map and then combines various linear transformation operations to enhance the feature map's information.This ensures feature diversity while efficiently extracting features.”
From A Modified YOLOv8 Detection Network for UAV Aerial Image Recognition (2023) · cites this paper as [32]
The cheap operation significantly reduces parameter cost and computational cost by reusing features and discards redundant information that may exist in conventional convolutions.
“This processing method significantly reduces the parameter cost and computational cost by reusing features and discards redundant information that may exist in conventional convolutions.”
From A Modified YOLOv8 Detection Network for UAV Aerial Image Recognition (2023) · cites this paper as [32]
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- Authors
- Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C.
- Affiliations
- Huawei Technologies (China) (CN)
- Peking University (CN)
- The University of Sydney (AU)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1577-1586
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035414587
- Cited by
- 5,222
- References
- 56
- Citations by year
- 2026: 756 · 2025: 1321 · 2024: 1275 · 2023: 1005 · 2022: 536 · 2021: 281 · 2020: 48
- FWCI
- 144.05
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Research Council (DE180101438)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- GhostNet, Ghost module, Ghost Bottleneck, lightweight convolutional neural network, linear operators, neural architecture design, embedded devices, ImageNet classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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