基于深度神经网络的交通标志识别系统内含架构图.zip

# Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods Pytorch Implementation of Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods * ÁlvaroArcos-GarcíaJuan A.Álvarez-GarcíaLuis M.Soria-Morillo* Deep neural network for traffic sign recognition systems: An analysisof spatial transformers and stochastic optimisation methods [link1](https://idus.us.es/xmlui/bitstream/handle/11441/80679/NEUNET-D-17-00381.pdf?sequence=1&isAllowed=y) [link2](https://reader.elsevier.com/reader/sd/pii/S0893608018300054?token=0656FA2921430AA401BA73A6990A187F32A6FBDD12EAA2FC87FD556B3CDDF6DA8D5BE54F230A979E57369C48AB081452 ) LCN Implementation is taken from https://github.com/dibyadas/Visualize-Normalizations ## Notes: - ASGD Works best among all optimizers for me for Learning Rate : 10^-2 - Class imbalance is removed prior to training by duplicating the data - Learning Rate Decay worsenes the performance - Data Augmentation, in general, decreases performance although Spatial Transformer model ensures augmentation isn't a problem. - Architecture is changed slighlty from the original set of layers - Currently Gaussian filter is kept constant for LCN, where as ideally it should be chosed at random during run-time Neural Net gives output of 6 neurons necessary for Affine transformation (translation, **cropping, rotation, scaling, and skewing)** and uses grid generator and sampling as inbuilt Pytorch commands    - *Max Jaderberg Karen Simonyan Andrew Zisserman Koray Kavukcuoglu* Spatial Transformer Networks (https://arxiv.org/pdf/1506.02025.pdf ) # Citing ### BibTeX ``` @misc{pathaktraffic, author = {Priyank Pathak}, title = {PyTorch Deep Neural Network for Traffic Sign Recognition Systems}, year = {2018}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/ppriyank/Deep-neural-network-for-traffic-sign-recognition-systems}} } ```