├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2016 ikki kishida 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Implementations of image recognition model 2 | 3 | Paper: [131116 Netowork In Network](https://arxiv.org/abs/1312.4400 "Network In Network") 4 | Implementation: https://github.com/nutszebra/network_in_network 5 | 6 | Paper: [131116 Netowork In Network](https://arxiv.org/abs/1312.4400 "Network In Network") 7 | Implementations: https://github.com/nutszebra/network_in_network_with_bn 8 | 9 | Paper: [140904 Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556 "Very Deep Convolutional Networks for Large-Scale Image Recognition") 10 | Implementaion: https://github.com/nutszebra/vgg 11 | 12 | Paper: [140917 Going deeper with convolutions](https://arxiv.org/abs/1409.4842 "Going deeper with convolutions") 13 | Impelementation: https://github.com/nutszebra/googlenet 14 | 15 | Paper: [150211 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167 "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift") 16 | Implementaion: https://github.com/nutszebra/googlenet_v2 17 | 18 | Paper: [151202 Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567 "Rethinking the Inception Architecture for Computer Vision") 19 | Implementaion: https://github.com/nutszebra/googlenet_v3 20 | 21 | Paper: [150206 Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://arxiv.org/abs/1502.01852 "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification") 22 | Implementaion: https://github.com/nutszebra/prelu_net 23 | 24 | Paper: [150722 Training Very Deep Networks](https://arxiv.org/abs/1507.06228 "Training Very Deep Networks") 25 | Implementaion: https://github.com/nutszebra/highway_networks 26 | 27 | Paper: [151210 Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385 "Deep Residual Learning for Image Recognition") 28 | Implementaion: https://github.com/nutszebra/original_residual_net 29 | 30 | Paper: [160223 Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261 "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning") 31 | Implementaion: soon 32 | 33 | Paper: [160224 SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360 "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size") 34 | Implementaion: https://github.com/nutszebra/squeeze_net 35 | 36 | Paper: [160316 Identity Mappings in Deep Residual Networks](https://arxiv.org/abs/1603.05027 "Identity Mappings in Deep Residual Networks") 37 | Implementaion: https://github.com/nutszebra/residual_net 38 | 39 | Paper: [160325 Resnet in Resnet: Generalizing Residual Architectures](https://arxiv.org/abs/1603.08029 "Resnet in Resnet: Generalizing Residual Architectures") 40 | Implementation: https://github.com/nutszebra/resnet_in_resnet 41 | 42 | 43 | Paper: [160330 Deep Networks with Stochastic Depth](https://arxiv.org/abs/1603.09382 "Deep Networks with Stochastic Depth") 44 | Implementaion: https://github.com/nutszebra/stochastic_depth 45 | 46 | Paper: [160520 Swapout: Learning an ensemble of deep architectures](https://arxiv.org/abs/1605.06465 "Swapout: Learning an ensemble of deep architectures") 47 | Implementaion: https://github.com/nutszebra/swapout 48 | 49 | Paper: [160523 Wide Residual Networks](https://arxiv.org/abs/1605.07146 "Wide Residual Networks") 50 | Implementaion: https://github.com/nutszebra/wide_residual_net 51 | 52 | Paper: [160524 FractalNet: Ultra-Deep Neural Networks without Residuals](https://arxiv.org/abs/1605.07648 "FractalNet: Ultra-Deep Neural Networks without Residuals") 53 | Implementaion: https://github.com/nutszebra/fractal_net 54 | 55 | Paper: [160528 Weighted Residuals for Very Deep Networks](https://arxiv.org/abs/1605.08831 "Weighted Residuals for Very Deep Networks") 56 | Implementaion: https://github.com/nutszebra/weighted_residual_net 57 | 58 | Paper: [160809 Residual Networks of Residual Networks: Multilevel Residual Networks](https://arxiv.org/abs/1608.02908 "Residual Networks of Residual Networks: Multilevel Residual Networks") 59 | Implementaion: https://github.com/nutszebra/residual_networks_of_residual_networks 60 | 61 | Paper: [160825 Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993 "Densely Connected Convolutional Networks") 62 | Implementaion: https://github.com/nutszebra/dense_net 63 | 64 | Paper: [161007 Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357 "Xception: Deep Learning with Depthwise Separable Convolutions") 65 | Implementaion: soon 66 | 67 | Paper:[161010 Deep Pyramidal Residual Networks](https://arxiv.org/abs/1610.02915 "Deep Pyramidal Residual Networks") 68 | Implementaion: https://github.com/nutszebra/pyramidal_residual_networks 69 | 70 | Paper: [161105 Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578 "Neural Architecture Search with Reinforcement Learning") 71 | Implementaion: https://github.com/nutszebra/neural_architecture_search_with_reinforcement_learning_appendix_a 72 | 73 | Paper: [161116 Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431 "Aggregated Residual Transformations for Deep Neural Networks") 74 | Implementaion: https://github.com/nutszebra/resnext 75 | 76 | Paper: [161205 Deep Pyramidal Residual Networks with Separated Stochastic Depth](https://arxiv.org/abs/1612.01230 "Deep Pyramidal Residual Networks with Separated Stochastic Depth") 77 | Implementaion: https://github.com/nutszebra/pyramidal_residual_networks_with_separated_stochastic_depth 78 | --------------------------------------------------------------------------------