spatial-transformer-network - A Tensorflow Implementation of Spatial Transformer Networks

  •        32

This is a Tensorflow implementation of Spatial Transformer Networks by Max Jaderberg, Karen Simonyan, Andrew Zisserman and Koray Kavukcuoglu, accompanying by two-part blog tutorial series. Spatial Transformer Networks (STN) is a differentiable module that can be inserted anywhere in ConvNet architecture to increase its geometric invariance. It effectively gives the network the ability to spatially transform feature maps at no extra data or supervision cost.



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gvnn is primarily intended for self-supervised learning using low-level vision. It is inspired by the Spatial Transformer Networks (STN) paper that appeared in NIPS in 2015 and its open source code made available by Maxime Oquab. The code is self contained i.e. the original implementation of STN by Maxime is also within the repository. STs were mainly limited to applying only 2D transformations to the input. We added a new set of transformations often needed for manipulating data in 3D geometric computer vision. These include the 3D counterparts of what were used in original STN together with a lot more new transformations and different M-estimators.

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I tried to implement the idea in Attention Is All You Need. They authors claimed that their model, the Transformer, outperformed the state-of-the-art one in machine translation with only attention, no CNNs, no RNNs. How cool it is! At the end of the paper, they promise they will make their code available soon, but apparently it is not so yet. I have two goals with this project. One is I wanted to have a full understanding of the paper. Often it's hard for me to have a good grasp before writing some code for it. Another is to share my code with people who are interested in this model before the official code is unveiled. I got a BLEU score of 17.14. (Recollect I trained with a small dataset, limited vocabulary) Some of the evaluation results are as follows. Details are available in the results folder.

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SPP_net - SPP_net : Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

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This is a re-implementation of the object detection algorithm described in the ECCV 2014 paper "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition". This re-implementation should reproduce the object detection results reported in the paper up to some statistical variance. The models used in the paper are trained/fine-tuned using cuda-convnet, while the model attached with this code is trained/fine-tuned using Caffe, for the ease of code release. The implementation of image classification training/testing has not been included, but the network configuration files can be found directly in this code.

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pytorch-openai-transformer-lm - A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI

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MicroCity is a framework for spatial analysis and simulation. It is lightweight, fast, portable, extendable and user friendly. Users can easily operate large GIS and Grid data and perform Spatial, Fractal, Network analysis and simulation.

Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

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osmnx - OSMnx: Python for street networks

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SpatiaLite - Spatial SQL

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bi-att-flow - Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization

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The model has ~2.5M parameters. The model was trained with NVidia Titan X (Pascal Architecture, 2016). The model requires at least 12GB of GPU RAM. If your GPU RAM is smaller than 12GB, you can either decrease batch size (performance might degrade), or you can use multi GPU (see below). The training converges at ~18k steps, and it took ~4s per step (i.e. ~20 hours). You can still omit them, but training will be much slower.

class-transformer - Proper decorator-based transformation / serialization / deserialization of plain javascript objects to class constructors

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Proper decorator-based transformation / serialization / deserialization of plain javascript objects to class constructors


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pix2pix-tensorflow - TensorFlow implementation of "Image-to-Image Translation Using Conditional Adversarial Networks"

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TensorFlow implementation of Image-to-Image Translation Using Conditional Adversarial Networks that learns a mapping from input images to output images. Note: To avoid the fast convergence of D (discriminator) network, G (generator) network is updated twice for each D network update, which differs from original paper but same as DCGAN-tensorflow, which this project based on.

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