Published May 31, 2019
| Version v1
Model
Open
Image classification model in nnp format trained using ImageNet-1k dataset
Description
This is a pre-trained image classification model trained using the Neural Network Console (github.com/sony/neural-network-console) and the ImageNet-1k Dataset.
Model Architecure: AlexNet, DenseNet-161, GoogLeNet, Inception-v3, MobileNet, MobileNet-v2, NIN, ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, ResNeXt-50, ResNeXt-101, SENet-154, ShuffleNet, ShuffleNet-0.5x, ShuffleNet-2.0x, SqueezeNet-1.0, SqueezeNet-1.1, VGG-11, VGG-13, VGG-16, Xception
Model Format: nnp file format(https://github.com/sony/nnabla/blob/master/doc/format.rst)
Files
Files
(3.7 GB)
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md5:1baa911a9a256bcd397a76887e3d343a
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md5:a95626f81a4ff3172d12e6c9b5c5780f
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md5:3697e611db6551c9700929a9ff5726af
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md5:96a023ef6ab843674327aa8f2d7dc95a
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92.5 MB | Download |
Additional details
References
- K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90.
- Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141).
- J. Deng, W. Dong, R. Socher, L. -J. Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 2009, pp. 248-255, doi: 10.1109/CVPR.2009.5206848.
- S. Xie, R. Girshick, P. Dollár, Z. Tu and K. He, "Aggregated Residual Transformations for Deep Neural Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 5987-5995, doi: 10.1109/CVPR.2017.634.
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1106–1114.
- Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going Deeper With Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9.
- Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818–2826.
- Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv preprint arXiv:1704.04861.
- Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4510–4520.
- Lin, M., Chen, Q., & Yan, S. (2013). Network in Network. arXiv preprint arXiv:1312.4400.
- Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6848–6856.
- Ma, N., Zhang, X., Zheng, H. T., & Sun, J. (2018). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the European Conference on Computer Vision, 122–138.
- Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv preprint arXiv:1602.07360.
- Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556.
- Chollet, F. (2017). Xception: Deep Learning With Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251–1258.