My thesis code for Traffic Sign Recognition using 2 different datasets (GTSRB and DFG) and different kinds of models (CNN, STN, ViT).
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Updated
Jan 6, 2023 - Jupyter Notebook
My thesis code for Traffic Sign Recognition using 2 different datasets (GTSRB and DFG) and different kinds of models (CNN, STN, ViT).
This study aims to show that group equivariant CNNs outperform spatial transformers, on tasks which demand rotation invariance, by providing theoretical background and experimental performance comparison with detailed analysis.
Foveated Spatial Transformers
Unofficial PyTorch implementation for incorporating a 3D Morphable Model (3DMM) into a Spatial Transformer Network (STN)
Spatial Transformer Networks (STN) implementation in TensorFlow
Recognizing traffic signs with deep learning and PyTorch using Spatial Transformer Convolutional Neural Networks.
ML framework to estimate Bayesian posteriors of galaxy morphological parameters
Implementation of STN (Spatial Transformer Network) and ICSTN (Inverse Compositional Spatial Transformer Networks) in Tensorlayer to predict transformation parameters from 2D images.
Image, point set, and surface registration in PyTorch.
Image-and-Spatial Transformer Networks
An unofficial PyTorch implementation of VoxelMorph- An unsupervised 3D deformable image registration method
Code for the paper "KISS: Keeping it Simple for Scene Text Recognition"
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