GhostNetV2: Enhance Cheap Operation with Long-Range Attention
Anonymous Author(s)
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Abstract
Light-weight convolutional neural networks (CNNs) are specially designed forapplications on mobile devices with faster inference speed yet modest performance.
The convolutional operation can only capture local information in a window region, which prevents performance from being further improved.
Introducing selfattention into convolution can capture global information well, but it will largelyencumber the actual speed.
In this paper, we propose a hardware-friendly attentionmechanism (dubbed DFC attention) and then present a new GhostNetV2 architecture for mobile applications.
The proposed DFC attention is constructed basedon fully-connected layers, which can not only execute fast on common hardwarebut also capture the dependence between long-range pixels.
We further revisit theexpressiveness bottleneck in previous GhostNet and propose to enhance expandedfeatures produced by cheap operations with DFC attention, so that a GhostNetV2block can aggregate local and long-range information simultaneously.
Extensiveexperiments demonstrate the superiority of GhostNetV2 over existing architectures.
For example, it achieves 75.3% top-1 accuracy on ImageNet with 167M FLOPs,signiﬁcantly suppressing GhostNetV1 (74.5%) with a similar computational cost.
Introduction
In computer vision, the architecture of deep neural network plays a vital role for various tasks, suchas image classiﬁcation [16, 8], object detection [25, 24] and video analysis [15].
In the past decade,the network architecture has been evolving rapidly and a series of milestones including AlexNet [16],
GoogleNet [27], ResNet [8] and EfﬁcientNet [30] have been developed.
These networks have pushedthe performances of a wide range of visual tasks to a high level.
To deploy neural networks on edge devices like smartphone and wearable devices we need to considernot only the performance of a model, but also its efﬁciency especially the actual inference speed.
Matrix multiplications occupy the main part of computational cost and parameters.
Developing lightweight models is a promising approach to reduce the inference latency.
MobileNet [11] factorizesa standard convolution into depthwise convolution and point-wise convolution, which reduces thecomputational cost drastically.
MobileNetV2 [26] and MobileNetV3 [10] further introduce theinverted residual block and improve the network architecture.
ShufﬂeNet [35] utilizes the shufﬂeoperation to encourage the information exchange between channel groups.
GhostNet [7] proposesthe cheap operation to reduce feature redundancy in channels.
These light-weight neural networkshave been applied in many mobile applications.
Nevertheless, the convolution-based light-weight models are weak in modeling long-range dependency, which limits further performance improvement.
Recently, Transformer-like models areintroduced to computer vision, in which the self-attention module can capture the global information.
The typical self-attention module requires quadratic complexity w.r.t.
the size of feature’s shape and [Figure] [Figure] is not computationally friendly.
Moreover, plenty of feature splitting and reshaping operations arerequired to calculate the attention map.
Though their theoretical complexity is negligible, these operations incur more memory usage and longer latency in practice.
Thus, utilizing vanilla self-attentionin light-weight models is not friendly for mobile deployment.
For example, MobileViT with massiveself-attention operations is more than 7 × slower than MobileNetV2 on ARM devices [21].
In this paper, we propose a new attention mechanism (dubbed DFC attention) to capture the longrange spatial information, while keeping the implementation efﬁciency of light-weight convolutionalneural networks.
Only fully connected (FC) layers participate in generating the attention maps forsimplicity.
Speciﬁcally, a FC layer is decomposed into horizontal FC and vertical FC to aggregatepixels in a 2D feature map of CNN.
The two FC layers involve pixels in a long range along theirrespective directions, and stacking them will produce a global receptive ﬁeld.
Moreover, startingfrom ate-of-the-art GhostNet, we revisit its representation bottleneck and enhance the intermediatefeatures with the DFC attention.
Then we construct a new light-weight vision backbone, GhostNetV2.
Compared with the existing architectures, it can achieve a better tread-off between accuracy andinference speed (as shown in Figures 1 and 2).
Related Work
It is a challenge to design a light-weight neural architecture with fast inference speed and highperformance simultaneously.
SqueezeNet [13] proposes three strategies to design a compact model,i.e. , replacing 3 × 3 ﬁlters with 1 × 1 ﬁlers, decreasing the number of input channels to 3 x 3 ﬁlters,and down-sampling late in the network to keep large feature maps.
These principles are constructive,especially the usage of 1 × 1 convolution.
MobileNetV1 [11] replaces almost all the 3 × 3 ﬁlers with1 × 1 kernel and depth-wise separable convolutions, which dramatically reduces the computationalcost.
MobileNetV2 [26] further introduces the residual connection to the light-weight model, andconstructs an inverted residual structure, where the intermediate layer of a block has more channelsthan its input and output.
To keep representation ability, a part of non-linear functions are removed.
MobileNeXt [36] rethinks the necessary of inverted bottleneck, and claims that the classic bottleneckstructure can also achieve high performance.
Considering the 1 × 1 convolution account for asubstantial part of computational cost, ShufﬂeNet [35] replace it with group convolution.
The channelshufﬂe operation to help the information ﬂowing across different groups.
By investigating the factorsthat affect the practical running speed, ShufﬂeNet V2 [20] proposes a hardware-friendly new block.
By leveraging the feature’s redundancy, GhostNet [7] replaces half channels in 1 × 1 convolutionwith cheap operations.
Until now, GhostNet has been the SOTA light-weight model with a goodtrade-off between accuracy and speed.
Besides manual design, a series of methods try to search for a light-weight architecture.
For example,
FBNet [34] designs a hardware-aware searching strategy, which can directly ﬁnd a good trade-offbetween accuracy and speed on a speciﬁc hardware.
Based on the inverted residual bottleneck,
MnasNet [29], MobileNetV3 [10] search the architecture parameters,such as model width, modeldepth, convolutional ﬁlter’s size, etc .
Though NAS based methods achieve high performance, theirsuccess is based on well-designed search spaces and architectural units.
Automatic searching andmanual design can be combined to ﬁnd a better architecture.
Preliminary3.1 A Brief Review of GhostNet
GhostNet [7] is SOTA light-weight model designed for efﬁcient inference on mobile devices.
Itsmain component is the Ghost module, which can replace the original convolution by generating morefeature maps from cheap operations.
Given input feature X ∈ R H × W × C with height H , width Wand channel’s number C , a typical Ghost module can replace a standard convolution by two steps.
Firstly, a 1 × 1 convolution is used to generate the intrinsic feature, i.e. ,
[Equation]
where ∗ denotes the convolution operation.
F 1 × 1 is the point-wise convolution, and Y (cid:48) ∈
R H × W × C (cid:48) out is the intrinsic features, whose sizes are usually smaller than the original outputfeatures, i.e. , C (cid:48) out < C out .
Then cheap operations ( e.g. , depth-wise convolution) are used to generatemore features based on the intrinsic features.
The two parts of features are concatenated along thechannel dimension, i.e. ,
[Equation]
where F dp is the depth-wise convolutional ﬁlter, and
Y ∈ R H
× W × C out is the output feature.
Though
Ghost module can reduce the computational cost signiﬁcantly, the representation ability is inevitablyweakened.
The relationship between spatial pixels is vital to make accurate recognition.
While in
GhostNet, the spatial information is only captured by the cheap operations (usually implemented by3 × 3 depth-wise convolution) for half of the features.
The remaining features are just produced by1 × 1 point-wise convolution, without any interaction with other pixels.
The weak ability to capturethe spatial information may prevent performance from being further improved.
A block of GhostNet is constructed by stacking two Ghost modules (shown in Figure 4(a)).
Similar to
MobileNetV2 [26], it is also an inverted bottleneck, i.e. , the ﬁrst Ghost module acts as an expansionlayer to increase the number of output channels, and the second Ghost module reduces the channels’number to match the shortcut path.
3.2 Revisit Attention for Mobile Architecture
[Table]
Originating from the NLP ﬁeld [31],attention-based models are introducedto computer vision recently.
Forexample, ViT [6] uses the standardtransformer model stacked by selfattention modules and MLP modules.
Wang et al.
insert the self-attention operation into convolutional neural networks to capture the non-local information [32].
A typical attention module usually has a quadratic complexity w.r.t.
the feature’s size, which is unscalable to high-resolutionimages in downstream tasks such as object detection and semantic segmentation.
A mainstream strategy to reduce attention’s complexity is splitting images into multiple windowsand implementing the attention operation inside windows or crossing windows.
For example, Swin
Transformer [19] splits the original feature into multiple non-overlapped windows, and the selfattention is calculated within the local windows.
MobileViT [21] also unfolds the feature intonon-overlapping patches and calculates the attention across these patches.
For the 2D feature map in
CNN, implementing the feature splitting and attention calculation involves plenty of tensor reshapingand transposing operations.
whose theoretical complexity is negligible.
In a large model ( e.g. ,
Swin-B [19] with several billion FLOPs) with high complexity, these operations only occupy a fewportions of the total inference time.
While for the light-weight models, their deploying latency cannotbe overlooked.
For an intuitive understanding, we equip the GhostNet model with the self-attention used in MobileViT [21] and measure the latency on Huawei P30 (Kirin 980 CPU) with TFLite tool. We usethe standard input’s resolution of ImageNet, i.e. , 224 × 224 , and show the results in Table 1.
Theattention mechanism only adds about 20% theoretical FLOPs, but requires 2 × inference time on a mobile device.
The large difference between theoretical and practical complexity shows that it isnecessary to design a hard-ware friendly attention mechanism for fast implementation on mobiledevices.
Approach
[Figure]
4.1 DFC Attention for Mobile Architecture
[Figure]
In this section, we will discuss how to design an attention module for mobile CNNs.
A desiredattention is expected to have the following properties:
• Long-range.
It is vital to capture the long-range spatial information for attention to enhance
the representation ability, as a light-weight CNN ( e
g. , MobileNet [11], GhostNet [7]) usually
adopts small convolution ﬁlters ( e.g. , 1 × 1 convolution) to save computational cost.
• Deployment-efﬁcient.
The attention module should be extremely efﬁcient to avoid slowing
the inference down.
Expensive transformations with high FLOPs or hardware-unfriendly
operations are unexpected.
• Concept-simple.
To keep the model’s generalization on diverse tasks, the attention module
should be conceptually-simple with little dainty design.
Though self-attention operations [6, 22, 19] can model the long-range dependence well, they are notdeployment-efﬁcient as discussed in the above section.
Compared with them, fully-connected (FC)layers with ﬁxed weights are simpler and easier to implement, which can also be used to generateattention maps with global receptive ﬁelds.
The detailed computational process is illustrated asfollows.
Given a feature Z ∈ R H × W × C , it can be seen as HW tokens z i ∈ R C , i.e. , Z ={ z 11 , z 12 , · · · , z HW } .
A direct implementation of FC layer to generate the attention map is formulated as:
[Equation]
where (cid:12) is element-wise multiplication, F is the learnable weights in the FC layer, and A ={ a 11 , a 12 , · · · , a HW } is the generated attention map.
Eq 3 can capture the global information byaggregating all the tokens together with learnable weights, which is much simpler than the typicalself-attention [31] as well.
However, its computational process still requires quadratic complexityw.r.t.
feature’s size ( i.e. , O ( H 2 W 2 ) ) 1 , which is unacceptable in practical scenarios especially whenthe input images are of high resolutions.
For example, the 4-th layer of GhostNet has a feature mapwith 3136 ( 56 × 56 ) tokens, which incurs prohibitively high complexity to calculate the attentionmap.
Actually, feature maps in a CNN are usually of low-rank [28, 14], it is unnecessary to connectall the input and output tokens in different spatial locations densely.
The feature’s 2D shape naturallyprovides a perspective to reduce the computation of FC layers, i.e. , decomposing Eq.
into two FClayers and aggregating features along the horizontal and vertical directions, respectively.
It can be
(a
[Figure]
bottleneck (b) GhostNetV2 bottleneck
Stride=
Ghost module
Add
Ghost module
BN ReLU
BN
DFC attention
Mul
Stride=
Ghost module
Add
Ghost module
BN ReLU
BN
DFC attention
Mul
DWConvStride=
BN
DFC attention Stride=
Ghost module
Add
Ghost module
BN ReLU
BN
Stride=
Ghost module
DWConvStride=
BN BN
Ghost module
BN ReLU
Add
Down-sample
Horizontal FC 1x1 Convolution
BN
Vertical FC
BN
Sigmoid formulated as:
[Equation]
where F H and F W are transformation weights.
Taking the original feature Z as input, Eq. 4 and Eq.
5are applied to the features sequentially, capturing the long-range dependence along the two directions,respectively.
We dub this operation as decoupled fully connected (DFC) attention, whose informationﬂow is shown in Figure 3.
Owing to the decoupling of horizontal and vertical transformations, thecomputational complexity of the attention module can be reduced to O
( H 2 W + HW 2 ) .
In the fullattention (Eq. 3), all the patches in a square region participate in the calculation of the focused patchdirectly. In DFC attention, a patch is directly aggregated by patches in its vertical/horizontal lines,while other patches participate in the generation of those patches in the vertical/horizontal lines,having an indirect relationship with the focused token. Thus the calculation of a patch also involvesall the patches in the square region. We visualize the attention in Figure 6 and show calculationprocess with diagrams (Figure A1) in the supplemental material.
Eqs. 4 and 5 denote the general formulation of DFC attention, which aggregates pixels alonghorizontal and vertical directions, respectively.
By sharing a part of transformation weights, it canbe conveniently implemented with convolutions, leaving out the time-consuming tensor reshapingand transposing operations that affect the practical inference speed.
To process input images withvarying resolutions, the ﬁlter’s size can be decoupled with feature map’s size, i.e. , two depth-wiseconvolutions with kernel sizes 1 × K H and K W × 1 are sequentially applied on the input feature.
When implemented with convolution, the theoretical complexity of DFC attention is denoted as O ( K H HW + K W HW ) .
This strategy is well supported by tools such as TFLite and ONNX forfast inference on mobile devices.
4.2 GhosetNet V
In this section, we use the DFC attention to improve the representation ability of lightweight modelsand then present the new vision backbone, GhostNetV2.
Enhancing Ghost module.
As discussed in 3.1, only half of features in Ghost module (Eqs. 1 and 2)interact with other pixels, which damages its ability to capture spatial information.
Hence we use
DFC attention to enhance Ghost module’s output feature Y for capturing long-range dependenceamong different spatial pixels.
The input feature X ∈ R H
× W × C is sent to two branches, i.e. , one is the Ghost module to produceoutput feature Y (Eqs. 1 and 2) , and the other is the DFC module to generate attention map A
(Eqs. 4and 5).
Recalling that in a typical self-attention [31], linear transformation layers are used to transforminput feature into query and key for calculating attention maps.
Similarly, we also implement a 1 × 1convolution to convert module’s input X into DFC’s input Z .
The ﬁnal output O ∈ R H
× W × C of themodule is the product of two branch’s output, i.e. ,
[Equation]
(6) where (cid:12) is the element-wise multiplication and Sigmoid is the scaling function to normalize theattention map A into range (0 , 1) .
The information aggregation process is shown in Figure 5.
With the same input, the Ghost moduleand DFC attention are two parallel branches extracting information from different perspectives.
Theoutput is their element-wise product, which contains information from both features of the Ghostmodule and attentions of the DFC attention module.
The calculation of each attention value involvespatches in a large range so that the output feature can contain information from these patches.
[Figure]
Feature downsampling.
As Ghost module (Eqs. 1 and 2) is an extremely efﬁcient operation, directly paralleling the DFC attention withit will introduces extra computational cost.
Hencewe reduce the feature’s size by down-samplingit both horizontally and vertically, so that all theoperations in DFC attention can be conducted onthe smaller features.
By default, the width andheight are both scaled to half of their originallengths, which reduces 75% FLOPs of DFC attention.
Then produced feature map is then upsampled to the original size to match the feature’ssize in Ghost branch.
We naively use the averagepooling and bilinear interpolation for downsampling and upsampling, respectively, whose impactsare empirically investigated in Table A8 in the supplemental material.
Noticing that directly implementing sigmoid (or hard sigmoid) function willincur longer latency, we also deploy the sigmoid function on the downsampled features to acceleratepractical inference.
Though the value of attention maps may not be limited in range (0,1) strictly, weempirically ﬁnd that its impact on the ﬁnal performance is negligible (Table A7 in the supplementalmaterial).
GhostV2 bottleneck.
GhostNet adopts an inverted residual bottleneck containing two Ghost modules,where the ﬁrst module produces expanded features with more channels, while the second onereduces channel’s number to get output features.
This inverted bottleneck naturally decouples the“expressiveness” and “capacity” of a model [26].
The former is measured by the expanded featureswhile the latter is reﬂected by the input/output domains of a block.
The original Ghost modulegenerates partial features via cheap operations, which damages both the expressiveness and thecapacity.
By investigating the performance difference of equipping DFC attention on the expandedfeatures or output features (Table 7 in Section 5.3), we ﬁnd that enhancing ‘expressiveness’ is moreeffective.
Hence we only multiply the expanded features with DFC attention.
Figure 4(b) shows the diagram of GhostV2 bottleneck.
A DFC attention branch is parallel with theﬁrst Ghost module to enhance the expanded features.
Then the enhanced features are sent to thesecond Ghost module for producing output features.
It captures the long-range dependence betweenpixels in different spatial locations and enhances the model’s expressiveness.
Experiments
In this section, we empirically investigate the proposed GhostNetV2 model.
We conduct experimentson the image classiﬁcation task with the large-scale ImageNet dataset [5].
To validate its generalization, we use GhostNetV2 as backbone and embed it into a light-weight object detection scheme
YOLOV3 [24].
Models with different backbone are compared on MS COCO dataset [18].
At last, weconduct extensive ablation experiments for better understanding GhostNetV2.
The practical latencyis measured on Huawei P30 (Kirin 980 CPU) with TFLite tool.
[Table]
5.1 Image Classiﬁcation on ImageNet
Setting.
The classiﬁcation experiments are conducted on the benchmark ImageNet (ILSVRC 2012)dataset, which contains 1.28M training images and 50K validation images from 1000 classes.
Wefollow the training setting in [7] and report results with single crop on ImageNet dataset.
All theexperiments are conducted with PyTorch [23] on NVIDIA Tesla V100 GPUs.
Results.
The performance comparison of different models on ImageNet is shown in Table 2, Figure 1and Figure 2.
Several light-weight models are selected as the competing methods.
GhostNet [7],
MobileNetV2 [26], MobileNetV3 [10], and ShufﬂeNet [35] are widely-used light-weight CNNmodels with SOTA performance.
By combing CNN and Transformer, MobileViT [22] is a newbackbone presented recently.
Compared with them, GhostNetV2 achieves signiﬁcantly higherperformance with lower computational cost.
For example, GhostNetV2 achieves 75.3% top-1accuracy with only 167 FLOPs, which signiﬁcantly outperform GhostNet V1 (74.5%) with similarcomputational cost (167M FLOPs).
Practical Inference Speed.
Considering the light-weight model is designed for mobile applications,we practically measure the inference latency of different models on an arm-based mobile phone,using the TFLite tool [4].
Owing to the deploying efﬁciency of DFC attention, GhostNetV2 alsoachieves a good trade-off between accuracy and practical speed.
For example, with similar inferencelatency ( e.g. , 37 ms), GhostNetV2 achieves 75.3% top-1 accuracy, which is obviously GhostNet V1with 74.5% top-1 accuracy.
5.2 Object Detection on COCO
Setting.
To validate the generalization of GhostNetV2, we further conduct experiments on the objectdetection task.
The experiments are conducted on MS COCO 2017 dataset, composing of 118k
[Table] [Table]
training images and 5k validation images.
We embed different backbone into a widely-used detectionhead, YOLOv3 [24] and follow the default training strategy provided by MMDetection 2 .
Speciﬁcally,based on the pre-trained weights on ImageNet, the models are ﬁne-tuned with SGD optimizer for 30epochs.
The batchsize is set to 192 and initial learning to 0.003.
The experiments are conducted withinput resolutions 320 × [Table]
Results.
Table 3 compares the proposed GhostNetV2 model with GhostNet V1.
With differentinput resolutions, GhostNetV2 shows obvious superiority to the GhostNet V1.
For example, withsimilar computational cost ( i.e. , 340M FLOPs with 320 × 320 input resolution), GhostNetV2 achieves22.3% mAP, which suppresses GhostNet V1 by 0.5 mAP.
We conclude that capturing the long-rangedependence is also vital for downstream tasks, and the proposed DFC attention can effectively endowa large receptive ﬁeld to the Ghost module, and then construct a more powerful and efﬁcient block.
5.3 Ablation Studies
In this section, we conduct extensive experiments to investigate the impact of each component in
GhostNetV2.
The experiments are conducted with GhostNetV2 1 × on ImageNet.
We include a partof experiments here and more discussions can be found in the supplemental material.
[Table]
Discussion with NAS-based lightweight nonlocal networks. Auto-NL [17] is a NAS-basedwork following the typical paradigm of selfattention ( i.e. , ( xx T ) x , or x ( xx T ) , where x isa vector), whose computational cost is saved byreducing the feature’s dimensions and replacingconvolution with light-weight depthwise convolution. It also requires ’einsum’, tensor reshaping, and transposing operations for practical implementation, which incur large latency. Sinceonly theoretical FLOPs are reported in [17], we measure its latency using the same devices for
GhostNetV2 (Huawei P30 with Kirin 980 CPU) and show the results in Table 5.
AutoNL suffersmuch higher latency (76.4ms v.s. 56.7ms) than GhostNet with lower accuracy (76.5% v.s. 76.9%).
NAS-based methods ( e.g. , Auto-NL [17], OFA [1]) and GhostNetV2 actually focus on differentaspects of designing architectures.
Auto-NL [17] searches the architecture’s conﬁguration (e.g.,location for inserting LightNL, channel’s number in each layer) to pursue high performance.
OFA [1]also searches the architecture conﬁgures for speciﬁc hardware and uses more training tricks (e.g.,progressive shrinking, knowledge distillation) to improve performance.
While GhostNetV2 focuseson how to design a hardware-friendly attention mechanism, which doesn’t optimize the networkarchitecture and training recipe.
Searching network’s conﬁguration and improving training recipehave the potential to further improve GhostNetV2’s performance.
[Table]
[Table]
Experiments with other models.
As a universal module, the DFC attention can also be embeddedinto other architectures for enhancing their performance.
The resultsof MobileNetV2 with differentattention modules are shown in Table 4.
SE [12] and CBAM [33] are two widely-used attentionmodules, and CA [9] is a SOTA method presented recently.
The proposed DFC attention achieveshigher performance than these existing methods.
For example, the proposed DFC attention improvesthe top-1 accuracy of MobileNetV2 by 2.4%, which suppresses CA (1.5%) by a large margin. [Table]
The impact of kernel size in DFC attention.
We split the GhostNetV2 architecture into 3 stages by the feature’s size, and apply DFC attention with different kernel size (Table 8).
The kernel sizes1 × 3 and 3 × 1 cannot capture the long-range dependence well,which results in the worst performance ( i.e. , 74.8%).
Increasing thekernel size to capture the longer range information can signiﬁcantlyimprove the performance.
The location for implementing DFC attention.
The GhostNetV2model can be split into 4 stages by the feature’s size, and we empirically investigate how the implementing location affects the ﬁnalperformance.
The results are shown in Table 6, which empiricallyshows that the DFC attention can improve performance when implementing it on any stage.
Exhaustively adjusting or searching for proper locations has the potential to further improve the trade-offbetween accuracy and computational cost, which exceeds the scope of this paper.
By default, wedeploy the DFC attention on all the layers.
Enhancing expressiveness or capacity.
We implement the DFC attention on two Ghost modulesand show the results in Table 7.
As discussed in Section 4.2, the former enhances expanded features(expressiveness) while the latter improves the block’s capacity.
With similar computational costs,enhancing the expanded features brings 1.4% top-1 accuracy improvement, which is much higherthan enhancing the output feature.
Though enhancing both of the features can further improve theperformance, the computational cost also increases accordingly.
By default, we only enhance theexpanded features in an inverse residual bottleneck.
[Figure]
Visualization of decoupled attention and full attention. We visualize the decoupled attention produced by stacking vertical and horizontal attentionsand compare it with full attention.
In low layers, thedecoupled attention shows some cross-shaped patterns, indicating patches from the vertical/horizontallines participate more. As the depth increases, thepattern of the attention map diffuses and becomesmore similar to the full attention.
More discussionscan be found in the supplemental material.
Conclusion
This paper proposes a hardware-friendly DFC attention and presents a new GhostNetV2 architecturefor mobile applications.
The DFC attention can capture the dependence between pixels in long-rangespatial locations, which signiﬁcantly enhances the expressiveness ability of light-weight models.
Itdecomposes a FC layer into horizontal FC and vertical FC, which has large receptive ﬁelds along thetwo directions, respectively.
Equipped this computation-efﬁcient and deployment-simple modules,
GhostNetV2 can achieve a better trade-off between accuracy and speed.
Extensive experiments onbenchmark datasets ( e.g. , ImageNet, MS COCO) validate the superiority of GhostNetV2.
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