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xshhhm

Discovered public repositories for xshhhm in the GitHub catalog.

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xshhhm/minerva

Minerva: a fast and flexible tool for deep learning. It provides ndarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy. Please refer to the examples to see how multi-GPU setting is used.

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xshhhm/plex

Github project for continued development of PLEX wordspotting.

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xshhhm/smile

Statistical Machine Intelligence & Learning Engine

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xshhhm/cudamat

Python module for performing basic dense linear algebra computations on the GPU using CUDA.

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xshhhm/neuraltalk

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

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xshhhm/statismo

Framework for building Statistical Image And Shape Models

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xshhhm/HoG_SSE

Implementation of HoG feature extractor that uses SSE instructions.

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xshhhm/stanford_dl_ex

Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial

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xshhhm/Theano

Optimizing GPU-meta-programming code generating array oriented optimizing math compiler in Python

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xshhhm/rp

Random Prim's Algorithm for Object Proposals

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xshhhm/ews

Segmentation-free Word Spotting with Exemplar SVMs

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xshhhm/mser

Linear time Maximally Stable Extremal Regions implementation

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xshhhm/text_extraction

This code is the implementation of the method proposed in the paper “Multi-script text extraction from natural scenes” (Gomez & Karatzas) to appear in ICDAR2013 conference.

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xshhhm/Install-OpenCV

shell scripts to install different version of OpenCV in different distributions of Linux

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xshhhm/characterness

Code of "Characterness: An Indicator of Text in the Wild", IEEE Transcations on Image Processing

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xshhhm/ccv

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library

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xshhhm/DeepLearnToolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

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