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Rethinking the Inception Architecture for Computer Vision Christian Szegedy Google Inc. szegedy@google. com Vincent Vanhoucke vanhoucke@google. com Sergey Ioffe sioffe@google. com Jonathon Shlens shlens@google. com Zbigniew Wojna University College London zbigniewwojna@gmail. com Abstract Convolutional networks are at ... | Googles Inception v3.pdf |
it more difficult to make changes to the network. If the ar-chitecture is scaled up naively, large parts of the computa-tional gains can be immediately lost. Also, [20] does not provide a clear description about the contributing factors that lead to the various design decisions of the Goog Le Net architecture. This make... | Googles Inception v3.pdf |
Figure 1. Mini-network replacing the 5×5convolutions. one multiplication per activation. Therefore, any reduction in computational cost results in reduced number of param-eters. This means that with suitable factorization, we can end up with more disentangled parameters and therefore with faster training. Also, we can ... | Googles Inception v3.pdf |
Figure 3. Mini-network replacing the 3×3convolutions. The lower layer of this network consists of a 3×1convolution with 3 output units. 1x1 1x1 5x5 3x3 Pool 1x1 Base Filter Concat 1x1 Figure 4. Original Inception module as described in [20]. layers. Still we can ask the question whether one should factorize them into s... | Googles Inception v3.pdf |
1x1 1x1 1xn Pool 1x1 Base Filter Concat nx1 1xn nx1 1xn nx1 1x1 Figure 6. Inception modules after the factorization of the n×n convolutions. In our proposed architecture, we chose n= 7 for the17×17grid. (The filter sizes are picked using principle 3). graph, this means that original the hypothesis of [20] that these bra... | Googles Inception v3.pdf |
35x35x320 17x17x320 17x17x640 Pooling Inception 35x35x320 35x35x640 17x17x640 Inception Pooling Figure 9. Two alternative ways of reducing the grid size. The so-lution on the left violates the principle 1 of not introducing an rep-resentational bottleneck from Section 2. The version on the right is3times more expensive... | Googles Inception v3.pdf |
minimizing the cross entropy is equivalent to maximizing the log-likelihood of the correct label. For a particular ex-amplexwith labely, the log-likelihood is maximized for q(k) =δk,y, whereδk,yis Dirac delta, which equals 1for k=yand0otherwise. This maximum is not achievable for finitezkbut is approached if zy≫zkfor al... | Googles Inception v3.pdf |
Receptive Field Size Top-1 Accuracy (single frame) 79×79 75. 2% 151×151 76. 4% 299×299 76. 6% Table 2. Comparison of recognition performance when the size of the receptive field varies, but the computational cost is constant. does higher input resolution helps if the computational ef-fort is kept constant. One simple wa... | Googles Inception v3.pdf |
Network Models Evaluated Crops Evaluated Top-1 Error Top-5 Error VGGNet [18] 2-23. 7% 6. 8% Goog Le Net [20] 7 144-6. 67% PRe LU [6]---4. 94% BN-Inception [7] 6 144 20. 1% 4. 9% Inception-v3 4 144 17. 2% 3. 58%∗ Table 5. Ensemble evaluation results comparing multi-model, multi-crop reported results. Our numbers are com... | Googles Inception v3.pdf |
[16] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. 2014. [17] F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A uni-fied embedding for face recognition and clustering. ar Xiv preprint ar Xiv:1... | Googles Inception v3.pdf |
Under review as a conference paper at ICLR 2017 SQUEEZE NET: A LEXNET-LEVEL ACCURACY WITH 50X FEWER PARAMETERS AND <0. 5MB MODEL SIZE Forrest N. Iandola1, Song Han2, Matthew W. Moskewicz1, Khalid Ashraf1, William J. Dally2, Kurt Keutzer1 1Deep Scale∗& UC Berkeley2Stanford University {forresti, moskewcz, kashraf, keutze... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 As you can see, there are several advantages of smaller CNN architectures. With this in mind, we focus directly on the problem of identifying a CNN architecture with fewer parameters but equivalent accuracy compared to a well-known model. We have discovered such an archit... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 Perhaps the mostly widely studied CNN macroarchitecture topic in the recent literature is the impact ofdepth (i. e. number of layers) in networks. Simoyan and Zisserman proposed the VGG (Simonyan & Zisserman, 2014) family of CNNs with 12 to 19 layers and reported that dee... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 h"p://www. presenta. on0process. com/lego0blocks0in0powerpoint. html88 squeeze 8expand 81x18convolu. on8filters 81x18and83x38convolu. on8filters 8Re LU 8 Re LU 8 Figure 1: Microarchitectural view: Organization of convolution filters in the Fire module. In this example, s1x1=... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 "labrador retriever dog" conv196 fire2128 fire3128 fire4256 fire5256 fire6384 fire7384 fire8512 fire9512 conv101000 softmaxmaxpool/2 maxpool/2 maxpool/2global avgpool conv196 fire2128 fire3128 fire4256 fire5256 fire6384 fire7384 fire8512 fire9512 conv101000 softmaxmaxpool... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 ported the Squeeze Net CNN architecture for compatibility with a number of other CNN software frameworks: MXNet (Chen et al., 2015a) port of Squeeze Net: (Haria, 2016) Chainer (Tokui et al., 2015) port of Squeeze Net: (Bell, 2016) Keras (Chollet, 2016) port of Squeeze Net... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 Table 2: Comparing Squeeze Net to model compression approaches. By model size, we mean the number of bytes required to store all of the parameters in the trained model. CNN architecture Compression Approach Data Type Original Compressed Model Size Reduction in Model Size ... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 13#MB#of #weights #85. 3% #accuracy #86. 0% #accuracy #19#MB#of #weights #4. 8#MB#of #weights #80. 3% #accuracy #Squeeze Net # (a) Exploring the impact of the squeeze ratio ( SR) on model size and accuracy. 21#MB#of #weights #13#MB#of #weights #5. 7#MB#of #weights #85. 3%... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 The VGG (Simonyan & Zisserman, 2014) architectures have 3x3 spatial resolution in most layers' filters; Goog Le Net (Szegedy et al., 2014) and Network-in-Network (Ni N) (Lin et al., 2013) have 1x1 filters in some layers. In Goog Le Net and Ni N, the authors simply propose a... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 Table 3: Squeeze Net accuracy and model size using different macroarchitecture configurations Architecture Top-1 Accuracy Top-5 Accuracy Model Size Vanilla Squeeze Net 57. 5% 80. 3% 4. 8MB Squeeze Net + Simple Bypass 60. 4% 82. 5% 4. 8MB Squeeze Net + Complex Bypass 58. 8%... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G Berneshawi, Huimin Ma, Sanja Fidler, and Raquel Urtasun. 3d object proposals for accurate object class detection. In NIPS, 2015b. Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catan-... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level perfor-mance on imagenet classification. In ICCV, 2015a. Kaiming He and Jian Sun. Convolutional neural networks at constrained time cost. In CVPR, 2015. Kaiming He, Xiangyu Zhang, Shao... | SQUEEZENET.pdf |
Under review as a conference paper at ICLR 2017 Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Re-thinking the inception architecture for computer vision. ar Xiv:1512. 00567, 2015. Christian Szegedy, Sergey Ioffe, and Vincent Vanhoucke. Inception-v4, inception-resnet and the im... | SQUEEZENET.pdf |
ar Xiv:1409. 1556v6 [cs. CV] 10 Apr 2015Publishedasa conferencepaperat ICLR2015 VERYDEEPCONVOLUTIONAL NETWORKS FORLARGE-SCALEIMAGERECOGNITION Karen Simonyan∗& Andrew Zisserman+ Visual Geometry Group,Departmentof Engineering Science, Universityof Oxford {karen,az }@robots. ox. ac. uk ABSTRACT In this work we investigate... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 configurations are compared on the ILSVRC classification tas k in Sect. 4. Sect. 5 concludes the paper. For completeness,we also describeand assess our ILS VRC-2014object localisationsystem in Appendix A,anddiscussthegeneralisationofverydeepfe aturestootherdatasetsin Appendix B. Fi... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Table 1:Conv Net configurations (shown in columns). The depth of the configurations increase s fromtheleft(A)totheright(E),asmorelayersareadded(th eaddedlayersareshowninbold). The convolutional layer parameters are denoted as “conv ⟨receptive field size ⟩-⟨number of channels ⟩”. The... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 (22 weight layers) and small convolution filters (apart from 3×3, they also use 1×1and5×5 convolutions). Their network topology is, however, more co mplex than ours, and the spatial reso-lution of the feature maps is reduced more aggressively in th e first layers to decrease the am... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 model is trained to recognise objects over a wide range of sca les. For speed reasons, we trained multi-scale models by fine-tuning all layers of a single-sca le model with the same configuration, pre-trainedwithfixed S= 384. 3. 2 T ESTING Attest time,givena trained Conv Netandaninp... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 main evaluation criterion used in ILSVRC, and is computed as the proportion of images such that theground-truthcategoryisoutsidethetop-5predictedca tegories. Forthemajorityofexperiments,weusedthevalidationseta sthetestset. Certainexperimentswere also carried out on the test set a... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Theresults,presentedin Table4,indicatethatscalejitte ringattest timeleadstobetterperformance (as compared to evaluating the same model at a single scale, s hown in Table 3). As before, the deepest configurations(D and E) perform the best, and scale j ittering is better than traini... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Table6:Multiple Conv Netfusion results. Combined Conv Net models Error top-1 val top-5val top-5test ILSVRCsubmission (D/256/224,256,288), (D/384/352,384,416), (D/[256;512 ]/256,384,512) (C/256/224,256,288), (C/384/352,384,416) (E/256/224,256,288), (E/384/352,384,416)24. 7 7. 5 7.... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 REFERENCES Bell, S., Upchurch, P.,Snavely, N., and Bala, K. Material re cognition inthe wild withthe materials in context database. Co RR,abs/1412. 0623, 2014. Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. R eturn of the devil in the details: Delving deep intoconvolu... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. Image Net large sc ale visual recognition challenge. Co RR, abs/1409. 0575, 2014. Sermanet,P.,Eigen, D.,Zhang, X.,Mathieu, M.,Ferg... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 offsets technique of Sermanetetal. (2014), which increase s the spatial resolution of the bounding boxpredictionsandcanfurtherimprovetheresults. A. 2 L OCALISATION EXPERIMENTS In this section we first determine the best-performinglocal isation setting (using the first test proto-co... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Table 10: Comparison with the state of the art in ILSVRC localisation. Our methodis denoted as“VGG”. Method top-5val. error (%) top-5 testerror (%) VGG 26. 9 25. 3 Goog Le Net (Szegedyet al., 2014)-26. 7 Over Feat (Sermanet etal.,2014) 30. 0 29. 9 Krizhevsky et al. (Krizhevsky et... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 ilarly to the aggregation by stacking. We hypothesize that t his is due to the fact that in the VOC dataset the objects appear over a variety of scales, so there is no particular scale-specific seman-tics which a classifier could exploit. Since averaging has a b enefit of not inflati... | VGG-16 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Table 12: Comparison with the state of the art in single-image action c lassification on VOC-2012. Our models are denoted as “VGG”. Results marked with * were a chieved using Conv Nets pre-trainedonthe extended ILSVRCdataset (1512classes). Method VOC-2012 (mean AP) (Oquab et al., ... | VGG-16 layer image recognition model.pdf |
ar Xiv:1409. 1556v6 [cs. CV] 10 Apr 2015Publishedasa conferencepaperat ICLR2015 VERYDEEPCONVOLUTIONAL NETWORKS FORLARGE-SCALEIMAGERECOGNITION Karen Simonyan∗& Andrew Zisserman+ Visual Geometry Group,Departmentof Engineering Science, Universityof Oxford {karen,az }@robots. ox. ac. uk ABSTRACT In this work we investigate... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 configurations are compared on the ILSVRC classification tas k in Sect. 4. Sect. 5 concludes the paper. For completeness,we also describeand assess our ILS VRC-2014object localisationsystem in Appendix A,anddiscussthegeneralisationofverydeepfe aturestootherdatasetsin Appendix B. Fi... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Table 1:Conv Net configurations (shown in columns). The depth of the configurations increase s fromtheleft(A)totheright(E),asmorelayersareadded(th eaddedlayersareshowninbold). The convolutional layer parameters are denoted as “conv ⟨receptive field size ⟩-⟨number of channels ⟩”. The... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 (22 weight layers) and small convolution filters (apart from 3×3, they also use 1×1and5×5 convolutions). Their network topology is, however, more co mplex than ours, and the spatial reso-lution of the feature maps is reduced more aggressively in th e first layers to decrease the am... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 model is trained to recognise objects over a wide range of sca les. For speed reasons, we trained multi-scale models by fine-tuning all layers of a single-sca le model with the same configuration, pre-trainedwithfixed S= 384. 3. 2 T ESTING Attest time,givena trained Conv Netandaninp... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 main evaluation criterion used in ILSVRC, and is computed as the proportion of images such that theground-truthcategoryisoutsidethetop-5predictedca tegories. Forthemajorityofexperiments,weusedthevalidationseta sthetestset. Certainexperimentswere also carried out on the test set a... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Theresults,presentedin Table4,indicatethatscalejitte ringattest timeleadstobetterperformance (as compared to evaluating the same model at a single scale, s hown in Table 3). As before, the deepest configurations(D and E) perform the best, and scale j ittering is better than traini... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Table6:Multiple Conv Netfusion results. Combined Conv Net models Error top-1 val top-5val top-5test ILSVRCsubmission (D/256/224,256,288), (D/384/352,384,416), (D/[256;512 ]/256,384,512) (C/256/224,256,288), (C/384/352,384,416) (E/256/224,256,288), (E/384/352,384,416)24. 7 7. 5 7.... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 REFERENCES Bell, S., Upchurch, P.,Snavely, N., and Bala, K. Material re cognition inthe wild withthe materials in context database. Co RR,abs/1412. 0623, 2014. Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. R eturn of the devil in the details: Delving deep intoconvolu... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. Image Net large sc ale visual recognition challenge. Co RR, abs/1409. 0575, 2014. Sermanet,P.,Eigen, D.,Zhang, X.,Mathieu, M.,Ferg... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 offsets technique of Sermanetetal. (2014), which increase s the spatial resolution of the bounding boxpredictionsandcanfurtherimprovetheresults. A. 2 L OCALISATION EXPERIMENTS In this section we first determine the best-performinglocal isation setting (using the first test proto-co... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Table 10: Comparison with the state of the art in ILSVRC localisation. Our methodis denoted as“VGG”. Method top-5val. error (%) top-5 testerror (%) VGG 26. 9 25. 3 Goog Le Net (Szegedyet al., 2014)-26. 7 Over Feat (Sermanet etal.,2014) 30. 0 29. 9 Krizhevsky et al. (Krizhevsky et... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 ilarly to the aggregation by stacking. We hypothesize that t his is due to the fact that in the VOC dataset the objects appear over a variety of scales, so there is no particular scale-specific seman-tics which a classifier could exploit. Since averaging has a b enefit of not inflati... | VGG-19 layer image recognition model.pdf |
Publishedasa conferencepaperat ICLR2015 Table 12: Comparison with the state of the art in single-image action c lassification on VOC-2012. Our models are denoted as “VGG”. Results marked with * were a chieved using Conv Nets pre-trainedonthe extended ILSVRCdataset (1512classes). Method VOC-2012 (mean AP) (Oquab et al., ... | VGG-19 layer image recognition model.pdf |
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