1 Introduction
The local receptive fields (RFs) of neurons in the primary visual cortex (V1) of cats [14] have inspired the construction of Convolutional Neural Networks (CNNs) [26] in the last century, and it continues to inspire mordern CNN structure construction. For instance, it is wellknown that in the visual cortex, the RF sizes of neurons in the same area (e.g., V1 region) are different, which enables the neurons to collect multiscale spatial information in the same processing stage. This mechanism has been widely adopted in recent Convolutional Neural Networks (CNNs). A typical example is InceptionNets [42, 15, 43, 41], in which a simple concatenation is designed to aggregate multiscale information from, e.g., 33, 55, 77 convolutional kernels inside the “inception” building block.
However, some other RF properties of cortical neurons have not been emphasized in designing CNNs, and one such property is the adaptive changing of RF size. Numerous experimental evidences have suggested that the RF sizes of neurons in the visual cortex are not fixed, but modulated by the stimulus. The Classical RFs (CRFs) of neurons in the V1 region was discovered by Hubel and Wiesel [14], as determined by single oriented bars. Later, many studies (e.g., [30]) found that the stimuli outside the CRF will also affect the responses of neurons. The neurons are said to have nonclassical RFs (nCRFs). In addition, the size of nCRF is related to the contrast of the stimulus: the smaller the contrast, the larger the effective nCRF size [37]. Surprisingly, by stimulating nCRF for a period of time, the CRF of the neuron is also enlarged after removing these stimuli [33]. All of these experiments suggest that the RF sizes of neurons are not fixed but modulated by stimulus [38]
. Unfortunately, this property does not receive much attention in constructing deep learning models. Those models with multiscale information in the same layer such as InceptionNets have an inherent mechanism to adjust the RF size of neurons in the next convolutional layer according to the contents of the input, because the next convolutional layer linearly aggregates multiscale information from different branches. But that linear aggregation approach may be insufficient to provide neurons powerful adaptation ability.
In the paper, we present a nonlinear approach to aggregate information from multiple kernels to realize the adaptive RF sizes of neurons. We introduce a “Selective Kernel” (SK) convolution, which consists of a triplet of operators: Split, Fuse and Select. The Split operator generates multiple paths with various kernel sizes which correspond to different RF sizes of neurons. The Fuse operator combines and aggregates the information from multiple paths to obtain a global and comprehensive representation for selection weights. The Select operator aggregates the feature maps of differently sized kernels according to the selection weights.
The SK convolutions can be computationally lightweight and impose only a slight increase in parameter and computational cost. We show that on the ImageNet 2012 dataset [35] SKNets are superior to the previous stateoftheart models with similar model complexity. Based on SKNet50, we find the best settings for SK convolution and show the contribution of each component. To demonstrate their general applicability, we also provide compelling results on smaller datasets, CIFAR10 and 100 [22], and successfully embed SK into small models (e.g., ShuffleNetV2 [27]).
To verify the proposed model does have the ability to adjust neurons’ RF sizes, we simulate the stimulus by enlarging the target object in natural images and shrinking the background to keep the image size unchanged. It is found that most neurons collect information more and more from the larger kernel path when the target object becomes larger and larger. These results suggest that the neurons in the proposed SKNet have adaptive RF sizes, which may underlie the model’s superior performance in object recognition.
2 Related Work
Multibranch convolutional networks. Highway networks [39] introduces the bypassing paths along with gating units. The twobranch architecture eases the difficulty to training networks with hundreds of layers. The idea is also used in ResNet [9, 10], but the bypassing path is the pure identity mapping. Besides the identity mapping, the shakeshake networks [7] and multiresidual networks [1] extend the major transformation with more identical paths. The deep neural decision forests [21] form the treestructural multibranch principle with learned splitting functions. FractalNets [25] and Multilevel ResNets [52] are designed in such a way that the multiple paths can be expanded fractally and recursively. The InceptionNets [42, 15, 43, 41] carefully configure each branch with customized kernel filters, in order to aggregate more informative and multifarious features. Please note that the proposed SKNets follow the idea of InceptionNets with various filters for multiple branches, but differ in at least two important aspects: 1) the schemes of SKNets are much simpler without heavy customized design and 2) an adaptive selection mechanism for these multiple branches is utilized to realize adaptive RF sizes of neurons.
Grouped/depthwise/dilated convolutions. Grouped convolutions are becoming popular due to their low computational cost. Denote the group size by , then both the number of parameters and the computational cost will be divided by , compared to the ordinary convolution. They are first adopted in AlexNet [23] with a purpose of distributing the model over more GPU resources. Surprisingly, using grouped convolutions, ResNeXts [47] can also improve accuracy. This is called “cardinality”, which characterize the model together with depth and width.
Many compact models such as IGCV1 [53], IGCV2 [46] and IGCV3 [40] are developed, based on the interleaved grouped convolutions. A special case of grouped convolutions is depthwise convolution, where the number of groups is equal to the number of channels. Xception [3] and MobileNetV1 [11] introduce the depthwise separable convolution which decomposes ordinary convolutions into depthwise convolution and pointwise convolution. The effectiveness of depthwise convolutions is validated in the subsequent works such as MobileNetV2 [36] and ShuffleNet [54, 27]. Beyond grouped/depthwise convolutions, dilated convolutions [50, 51] support exponential expansion of the RF without loss of coverage. For example, a 33 convolution with dilation 2 can approximately cover the RF of a 55 filter, whilst consuming less than half of the computation and memory. In SK convolutions, the kernels of larger sizes (e.g., 1) are designed to be integrated with the grouped/depthwise/dilated convolutions, in order to avoid the heavy overheads.
Attention mechanisms.
Recently, the benefits of attention mechanism have been shown across a range of tasks, from neural machine translation
[2]in natural language processing to image captioning
[49] in image understanding. It biases the allocation of the most informative feature expressions [16, 17, 24, 28, 31] and simultaneously suppresses the less useful ones. Attention has been widely used in recent applications such as person reID [4], image recovery [55], text abstraction [34] and lip reading [48]. To boost the performance of image classification, Wang et al. [44] propose a trunkandmask attention between intermediate stages of a CNN. An hourglass module is introduced to achieve the global emphasis across both spatial and channel dimension. Furthermore, SENet [12] brings an effective, lightweight gating mechanism to selfrecalibrate the feature map via channelwise importances. Beyond channel, BAM [32] and CBAM [45] introduce spatial attention in a similar way. In contrast, our proposed SKNets are the first to explicitly focus on the adaptive RF size of neurons by introducing the attention mechanisms.Dynamic convolutions.Spatial Transform Networks [18] learns a parametric transformation to warp the feature map, which is considered difficult to be trained. Dynamic Filter [20] can only adaptively modify the parameters of filters, without the adjustment of kernel size. Active Convolution [19] augments the sampling locations in the convolution with offsets. These offsets are learned endtoend but become static after training, while in SKNet the RF sizes of neurons can adaptively change during inference. Deformable Convolutional Networks [6] further make the location offsets dynamic, but it does not aggregate multiscale information in the same way as SKNet does.
Output  ResNeXt50 (324d)  SENet50  SKNet50 
112 112 
7 7, 64, stride 2 

56 56 
3 3 max pool, stride 2 

56 56  3  3  3 
28 28  4  4  4 
14 14  6  6  6 
7 7  3  3  
1 1  7 7 global average pool, 1000d , softmax  
#P  25.0M  27.7M  27.5M 
GFLOPs  4.24  4.25  4.47 
3 Methods
3.1 Selective Kernel Convolution
To enable the neurons to adaptively adjust their RF sizes, we propose an automatic selection operation, “Selective Kernel” (SK) convolution, among multiple kernels with different kernel sizes. Specifically, we implement the SK convolution via three operators – Split, Fuse and Select, as illustrated in Fig. 1, where a twobranch case is shown. Therefore in this example, there are only two kernels with different kernel sizes, but it is easy to extend to multiple branches case.
Split: For any given feature map , by default we first conduct two transformations and with kernel sizes 3 and 5, respectively. Note that both and
are composed of efficient grouped/depthwise convolutions, Batch Normalization
[15]and ReLU
[29] function in sequence. For further efficiency, the conventional convolution with a 55 kernel is replaced with the dilated convolution with a 33 kernel and dilation size 2.Fuse: As stated in Introduction, our goal is to enable neurons to adaptively adjust their RF sizes according to the stimulus content. The basic idea is to use gates to control the information flows from multiple branches carrying different scales of information into neurons in the next layer. To achieve this goal, the gates need to integrate information from all branches. We first fuse results from multiple (two in Fig. 1) branches via an elementwise summation:
(1) 
then we embed the global information by simply using global average pooling to generate channelwise statistics as . Specifically, the th element of is calculated by shrinking through spatial dimensions :
(2) 
Further, a compact feature is created to enable the guidance for the precise and adaptive selections. This is achieved by a simple fully connected (fc) layer, with the reduction of dimensionality for better efficiency:
(3) 
where is the ReLU function [29], denotes the Batch Normalization [15], . To study the impact of on the efficiency of the model, we use a reduction ratio to control its value:
(4) 
where denotes the minimal value of ( 32 is a typical setting in our experiments).
Select: A soft attention across channels is used to adaptively select different spatial scales of information, which is guided by the compact feature descriptor . Specifically, a softmax operator is applied on the channelwise digits:
(5) 
where and
denote the soft attention vector for
and , respectively. Note that is the th row of and is the th element of , likewise and . In the case of two branches, the matrix is redundant because . The final feature map is obtained through the attention weights on various kernels:(6) 
where , . Note that here we provide a formula for the twobranch case and one can easily deduce situations with more branches by extending Eqs. (1) (5) (6).
3.2 Network Architecture
Using the SK convolutions, the overall SKNet architecture is listed in Table 1. We start from ResNeXt [47] for two reasons: 1) it has low computational cost with extensive use of grouped convolution, and 2) it is one of the stateoftheart network architectures with high performance on object recognition. Similar to the ResNeXt [47], the proposed SKNet is mainly composed of a stack of repeated bottleneck blocks, which are termed “SK units”. Each SK unit consists of a sequence of 11 convolution, SK convolution and 11 convolution. In general, all the large kernel convolutions in the original bottleneck blocks in ResNeXt are replaced by the proposed SK convolutions, enabling the network to choose appropriate RF sizes in an adaptive manner. As the SK convolutions are very efficient in our design, SKNet50 only leads to 10% increase in the number of parameters and 5% increase in computational cost, compared with ResNeXt50.
In SK units, there are three important hyperparameters which determine the final settings of SK convolutions: the number of paths that determines the number of choices of different kernels to be aggregated, the group number that controls the cardinality of each path, and the reduction ratio that controls the number of parameters in the fuse operator (see Eq. (4)). In Table 1, we denote one typical setting of SK convolutions SK[] to be SK[2, 32, 16]. The choices and effects of these parameters are discussed in Sec. 4.3.
Table 1 shows the structure of a 50layer SKNet which has four stages with {3,4,6,3} SK units, respectively. By varying the number of SK units in each stage, one can obtain different architectures. In this study, we have experimented with other two architectures, SKNet26, which has {2,2,2,2} SK units, and SKNet101, which has {3,4,23,3} SK units, in their respective four stages.
Note that the proposed SK convolutions can be applied to other lightweight networks, e.g., MobileNet [11, 36], ShuffleNet [54, 27], in which 33 depthwise convolutions are extensively used. By replacing these convolutions with the SK convolutions, we can also achieve very appealing results in the compact architectures (see Sec. 4.1).
4 Experiments
4.1 ImageNet Classification
The ImageNet 2012 dataset [35] comprises 1.28 million training images and 50K validation images from 1,000 classes. We train networks on the training set and report the top1 errors on the validation set. For data augmentation, we follow the standard practice and perform the randomsize cropping to 224 224 and random horizontal flipping [42]. The practical mean channel subtraction is adpoted to normalize the input images for both training and testing. Labelsmoothing regularization [43]
is used during training. For training large models, we use synchronous SGD with momentum 0.9, a minibatch size of 256 and a weight decay of 1e4. The initial learning rate is set to 0.1 and decreased by a factor of 10 every 30 epochs. All models are trained for 100 epochs from scratch on 8 GPUs, using the weight initialization strategy in
[8]. For training lightweight models, we set the weight decay to 4e5 instead of 1e4, and we also use slightly less aggressive scale augmentation for data preprocessing. Similar modifications can as well be referenced in [11, 54] since such small networks usually suffer from underfitting rather than overfitting. To benchmark, we apply a centre crop on the validation set, where 224224 or 320320 pixels are cropped for evaluating the classification accuracy. The results reported on ImageNet are the averages of 3 runs by default.top1 err (%)  #P  GFLOPs  
224  320  
ResNeXt50  22.23  21.05  25.0M  4.24 
AttentionNeXt56 [44]  21.76  –  31.9M  6.32 
InceptionV3 [43]  –  21.20  27.1M  5.73 
ResNeXt50 + BAM [32]  21.70  20.15  25.4M  4.31 
ResNeXt50 + CBAM [45]  21.40  20.38  27.7M  4.25 
SENet50 [12]  21.12  19.71  27.7M  4.25 
SKNet50 (ours)  20.79  19.32  27.5M  4.47 
ResNeXt101  21.11  19.86  44.3M  7.99 
Attention92 [44]  –  19.50  51.3M  10.43 
DPN92 [5]  20.70  19.30  37.7M  6.50 
DPN98 [5]  20.20  18.90  61.6M  11.70 
InceptionV4 [41]  –  20.00  42.0M  12.31 
InceptionResNetV2 [41]  –  19.90  55.0M  13.22 
ResNeXt101 + BAM [32]  20.67  19.15  44.6M  8.05 
ResNeXt101 + CBAM [45]  20.60  19.42  49.2M  8.00 
SENet101 [12]  20.58  18.61  49.2M  8.00 
SKNet101 (ours)  20.19  18.40  48.9M  8.46 
Comparisons with stateoftheart models. We first compare SKNet50 and SKNet101 to the public competitive models with similar model complexity. The results show that SKNets consistently improve performance over the stateoftheart attentionbased CNNs under similar budgets. Remarkably, SKNet50 outperforms ResNeXt101 by above absolute 0.32%, although ResNeXt101 is 60% larger in parameter and 80% larger in computation. With comparable or less complexity than InceptionNets, SKNets achieve above absolute 1.5% gain of performance, which demonstrates the superiority of adaptive aggregation for multiple kernels. We also note that using slightly less parameters, SKNets can obtain 0.30.4% gains to SENet counterparts in both 224224 and 320320 evaluations.
top1 err. (%)  #P  GFLOPs  
ResNeXt50 (324d)  22.23  25.0M  4.24 
SKNet50 (ours)  20.79 (1.44)  27.5M  4.47 
ResNeXt50, wider  22.13 (0.10)  28.1M  4.74 
ResNeXt56, deeper  22.04 (0.19)  27.3M  4.67 
ResNeXt50 (364d)  22.00 (0.23)  27.6M  4.70 
Selective Kernel vs. Depth/Width/Cardinality. Compared with ResNeXt (using the setting of 324d), SKNets inevitably introduce a slightly increase in parameter and computation due to the additional paths of differnet kernels and the selection process. For fair comparison, we increase the complexity of ResNeXt by changing its depth, width and cardinality, to match the complexity of SKNets. Table 3 shows that increased complexity does lead to better prediction accuracy.
However, the improvement is marginal when going deeper (0.19% from ResNeXt50 to ResNeXt53) or wider (0.1% from ResNeXt50 to ResNeXt50 wider), or with slightly more cardinality (0.23% from ResNeXt50 (324d) to ResNeXt50 (364d)).
In contrast, SKNet50 obtains 1.44% absolute improvement over the baseline ResNeXt50, which indicates that SK convolution is very efficient.
Performance with respect to the number of parameters. We plot the top1 error rate of the proposed SKNet with respect to the number of parameters in it (Fig. 2). Three architectures, SK26, SKNet50 and SKNet101 (see Section 3.2 for details), are shown in the figure. For comparison, we plot the results of some stateoftheart models including ResNets [9], ResNeXts [47], DenseNets [13], DPNs [5] and SENets [12] in the figure. Each model has multiple variants. The details of the compared architectures are provided in the Supplementary Materials. All Top1 errors are reported in the references. It is seen that SKNets utilizes parameters more efficiently than these models. For instance, achieving 20.2 top1 error, SKNet101 needs 22% fewer parameters than DPN98.
ShuffleNetV2  top1 err.(%)  MFLOPs  #P 
0.5 [27]  39.70  41  1.4M 
0.5 (our impl.)  38.41  40.39  1.40M 
0.5 + SE [12]  36.34  40.85  1.56M 
0.5 + SK  35.35  42.58  1.48M 
1.0 [27]  30.60  146  2.3M 
1.0 (our impl.)  30.57  140.35  2.45M 
1.0 + SE [12]  29.47  141.73  2.66M 
1.0 + SK  28.36  145.66  2.63M 
Lightweight models. Finally, we choose the representative compact architecture – ShuffleNetV2 [27], which is one of the strongest light models, to evaluate the generalization ability of SK convolutions. By exploring different scales of models in Table 4, we can observe that SK convolutions not only boost the accuracy of baselines significantly but also perform better than SE [12] (achieving around absolute 1% gain). This indicates the great potential of the SK convolutions in applications on lowend devices.
4.2 CIFAR Classification
To evaluate the performance of SKNets on smaller datasets, we conduct more experiments on CIFAR10 and 100 [22]. The two CIFAR datasets [22] consist of colored natural scence images, with 3232 pixel each. The train and test sets contain 50k images and 10k images respectively. CIFAR10 has 10 classes and CIFAR100 has 100. We take the architectures as in [47] for reference: our networks have a single 33 convolutional layer, followed by 3 stages each having 3 residual blocks with SK convolution. We also apply SE blocks on the same backbone (ResNeXt29, 1632d) for better comparisons. More architectural and training details are provided in the supplemantary materials. Notably, SKNet29 achieves better or comparable performance than ResNeXt29, 1664d with 60% fewer parameters and it consistently outperforms SENet29 on both CIFAR10 and 100 with 22% fewer parameters.
Models  #P  CIFAR10  CIFAR100 
ResNeXt29, 1632d  25.2M  3.87  18.56 
ResNeXt29, 864d  34.4M  3.65  17.77 
ResNeXt29, 1664d  68.1M  3.58  17.31 
SENet29 [12]  35.0M  3.68  17.78 
SKNet29 (ours)  27.7M  3.47  17.33 
4.3 Ablation Studies
In this section, we report ablation studies on the ImageNet dataset to investigate the effectiveness of SKNet.
Settings  top1 err. (%)  #P  GFLOPs  Resulted Kernel  
Kernel  
33  3  32  20.97  27.5M  4.47  77 
33  2  32  20.79  27.5M  4.47  55 
33  1  32  20.91  27.5M  4.47  33 
55  1  64  20.80  28.1M  4.56  55 
77  1  128  21.18  28.1M  4.55  77 
K3  K5  K7  SK  top1 err. (%)  #P  GFLOPs 
22.23  25.0M  4.24  
25.14  25.0M  4.24  
25.51  25.0M  4.24  
21.76  26.5M  4.46  
20.79  27.5M  4.47  
21.82  26.5M  4.46  
20.97  27.5M  4.47  
23.64  26.5M  4.46  
23.09  27.5M  4.47  
21.47  28.0M  4.69  
20.76  29.3M  4.70 
The dilation and group number . The dilation and group number are two crucial elements to control the RF size. To study their effects, we start from the twobranch case and fix the setting 33 filter with dilation = 1 and group = 32 in the first kernel branch of SKNet50.
Under the constraint of similar overall complexity, there are two ways to enlarge the RF of the second kernel branch: 1) increase the dilation whilst fixing the group number , and 2) simultaneously increase the filter size and the group number .
Table 6 shows that the optimal settings for the other branch are those with kernel size 55 (the last column), which is larger than the first fixed kernel with size 33. It is proved beneficial to use different kernel sizes, and we attribute the reason to the aggregation of multiscale information.
There are two optimal configurations: kernel size 55 with = 1 and kernel size 33 with = 2, where the latter has slightly lower model complexity. In general, we empirically find that the series of 33 kernels with various dilations is moderately superior to the corresponding counterparts with the same RF (large kernels without dilations) in both performance and complexity.
Combination of different kernels. Next we investigate the effect of combination of different kernels. Some kernels may have size larger than 33, and there may be more than two kernels. To limit the search space, we only use three different kernels, called “K3” (standard 33 convolutional kernel), “K5” (33 convolution with dilation 2 to approximate 55 kernel size), and “K7” (33 with dilation 3 to approximate 77 kernel size). Note that we only consider the dilated versions of large kernels (55 and 77) as Table 6 has suggested. is fixed to 32. If “SK” in Table 7 is ticked, it means that we use the SK attention across the corresponding kernels ticked in the same row (the output of each SK unit is V in Fig. 1), otherwise we simply sum up the results with these kernels (then the output of each SK unit is U in Fig. 1) as a naive baseline model.
The results in Table 7 indicate that excellent performance of SKNets can be attributed to the use of multiple kernels and the adaptive selection mechanism among them. From Table 7, we have the following observations: (1) When the number of paths increases, in general the recognition error decreases. The top1 errors in the first block of the table ( = 1) are generally higher than those in the second block ( = 2), and the errors in the second block are generally higher than the third block ( = 3). (2) No matter = 2 or 3, SK attentionbased aggregation of multiple paths always achieves lower top1 error than the simple aggregation method (naive baseline model). (3) Using SK attention, the performance gain of the model from = 2 to = 3 is marginal (the top1 error decreases from 20.79% to 20.76%). For better tradeoff between performance and efficiency, = 2 is preferred.
3 in different SK units. (c): Average results over all image instances in the ImageNet validation set. Standard deviation is also plotted.
4.4 Analysis and Interpretation
To understand how adaptive kernel selection works, we analyze the attention weights by inputting same target object but in different scales. We take all the image instances from the ImageNet validation set, and progressively enlarge the central object from 1.0 to 2.0 via a central cropping and subsequent resizing (see top left in Fig. 3a,b).
First, we calculate the attention values for the large kernel (55) in each channel in each SK unit. Fig. 3a,b (bottom left) show the attention values in all channels for two randomly samples in SK_3_4, and Fig. 3c (bottom left) shows the averaged attention values in all channels across all validation images. It is seen that in most channels, when the target object enlarges, the attention weight for the large kernel (55) increases, which suggests that the RF sizes of the neurons are adaptively getting larger, which agrees with our expectation.
We then calculate the difference between the the mean attention weights associated with the two kernels (larger minus smaller) over all channels in each SK unit. Fig. 3a,b (right) show the results for two random samples at different SK units, and Fig. 3c (right) show the results averaged over all validation images. We find one surprising pattern about the role of adaptive selection across depth: The larger the target object is, the more attention will be assigned to larger kernels by the Selective Kernel mechanism in low and middle level stages (e.g., SK_2_3, SK_3_4). However, at much higher layers (e.g., SK_5_3), all scale information is getting lost and such a pattern disappears.
Further, we look deep into the selection distributions from the perspective of classes. For each category, we draw the average mean attention differences on the representative SK units for 1.0 and 1.5 objects over all the 50 images which belong to that category. We present the statistics of 1,000 classes in Fig. 4. We observe the previous pattern holds true for all 1,000 categories, as illustrated in Fig. 4, where the importance of kernel 55 consistently and simultaneously increases when the scale of targets grows. This suggests that in the early parts of networks, the appropriate kernel sizes can be selected according to the semantic awareness of objects’ sizes, thus it efficiently adjusts the RF sizes of these neurons. However, such pattern is not existed in the very high layers like SK_5_3, since for the highlevel representation, “scale” is partially encoded in the feature vector, and the kernel size matters less compared to the situation in lower layers.
5 Conclusion
Inspired by the adaptive receptive field (RF) sizes of neurons in visual cortex, we propose Selective Kernel Networks (SKNets) with a novel Selective Kernel (SK) convolution, to improve the efficiency and effectiveness of object recognition by adaptive kernel selection in a softattention manner. SKNets demonstrate stateoftheart performances on various benchmarks, and from large models to tiny models. In addition, we also discover several meaningful behaviors of kernel selection across channel, depth and category, and empirically validate the effective adaption of RF sizes for SKNets, which leads to a better understanding of its mechanism. We hope it may inspire the study of architectural design and search in the future.
Acknowledgments The authors would like to thank the editor and the anonymous reviewers for their critical and constructive comments and suggestions. This work was supported by the National Science Fund of China under Grant No. U1713208, Program for Changjiang Scholars and National Natural Science Foundation of China, Grant no. 61836014.
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Appendix A Details of the Compared Models in Table 3
We provide more details for the variants of ResNeXt50 in Table 3 in the main body of the paper. Compared with this baseline, “ResNeXt50, wider” has more channels in all bottleneck blocks; “ResNeXt56, deeper” has extra 2 blocks in the end of the fourth stage of ResNeXt50; “ResNeXt50 (364d)” has a cardinality of 36 instead of 32. These three structures match the overall complexity of SKNet50, which makes the comparisons fair.
Appendix B Details of the Models in Table 4
For fair comparisons, we reimplement the ShuffleNetV2 [27] with 0.5 and 1.0 settings (see [27] for details). Our implementation changes the numbers of blocks in the three stages from {4,8,4} to {4,6,6}, therefore the performances and computational costs are slightly different from those reported in the original paper [27] (see Table 4 in the main body of the paper for detailed results). In Table 4, “+ SE” means that the SE module [12] is integrated after each shuffle layer in ShuffleNetV2, “+ SK” means that each 33 depthwise convolution is replaced by a SK unit with = 2 (K3 and K5 kernels are used in the two paths, respectively), = 4 and is the same as the number of channels in the corresponding stage due to the depthwise convolution.
Note that the 3
3 depthwise convolution in the original ShuffleNet is not followed by a ReLU activation function. We verify that the best practice for integrating SK units into ShuffleNet is also without ReLU activation functions in both paths in each SK unit (Table
S8).K3 + ReLU ?  K5 + ReLU ?  Top1 error (%) 
✓  ✗  28.65 
✗  ✓  28.40 
✗  ✗  28.36 
✓  ✓  28.49 
Appendix C Details of the Compared Models in Figure 2
We have plotted the results of some stateoftheart models including ResNet, ResNeXt, DenseNet, DPN and SENet in Figure 2 in the main body of the paper. Each dot represents a variant of certain model. Table S9 shows the settings of these variants, the numbers of parameters, and the evaluation results on the ImageNet validation set. Note that SENets are based on the corresponding ResNeXts.
Method  #P  Top1 error (%) 
ResNet50 [9]  25.56M  23.9 
ResNet101 [9]  44.55M  22.6 
ResNet152 [9]  60.19M  21.7 
DenseNet169 (k=32) [13]  14.15M  23.8 
DenseNet201 (k=32) [13]  20.01M  22.6 
DenseNet264 (k=32) [13]  33.34M  22.2 
DenseNet232 (k=48) [13]  55.80M  21.3 
ResNeXt50 (324d) [47]  25.00M  22.2 
ResNeXt101 (324d) [47]  44.30M  21.2 
DPN68 (324d) [5]  12.61M  23.7 
DPN92 (323d) [5]  37.67M  20.7 
DPN98 (324d) [5]  61.57M  20.2 
SENet50 [12]  27.7M  21.12 
SENet101 [12]  49.2M  20.58 
SKNet26  16.8M  22.74 
SKNet50  27.5M  20.79 
SKNet101  48.9M  20.19 
Appendix D Implementation Details on CIFAR Datasets (Section 4.2)
On CIFAR10 and CIFAR100 datasets, all networks are trained on 2 GPUs with a minibatch size 128 for 300 epochs. The initial learning rate is 0.1 for CIFAR10 and 0.05 for CIFAR100, and is divided by 10 at 50% and 75% of the total number of training epochs. Following [9], we use a weight decay of 5e4 and a momentum of 0.9. We adopt the weight initialization method introduced in [8]. The ResNeXt29 backbone is described in [47]. Based on it, SENet29 applies SE unit before each residual connection, and SKNet29 modifies the grouped 33 convolution to SK convolution with setting SK[2, 16, 32]. In order to prevent overfitting on these smaller datasets, we replace the 55 kernel in the second path in the SK unit to 11, while the setting for the first path remains the same.
Appendix E More Examples of Dynamic Selection
Figure S5 shows attention results for more images with three differently sized targets. Same as in Figure 3 in the main body of the paper, we see a trend in low and middle level stages: the larger the target object is, the more attention is assigned to larger kernels by the dynamic selection mechanism.
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