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Created by Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas from Stanford University. This work is based on our arXiv tech report, which is going to appear in CVPR 2017. We proposed a novel deep net architecture for point clouds (as unordered point sets). You can also check our project webpage for a deeper introduction.
Welcome to our training guide for inference and deep vision runtime library for NVIDIA DIGITS and Jetson Xavier/TX1/TX2. This repo uses NVIDIA TensorRT for efficiently deploying neural networks onto the embedded platform, improving performance and power efficiency using graph optimizations, kernel fusion, and half-precision FP16 on the Jetson.
NiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead. NiftyNet is not intended for clinical use.
Created by Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas from Stanford University. This work is based on our NIPS'17 paper. You can find arXiv version of the paper here or check project webpage for a quick overview. PointNet++ is a follow-up project that builds on and extends PointNet. It is version 2.0 of the PointNet architecture.
Kagome is an open source Japanese morphological analyzer written in pure golang. The MeCab-IPADIC and UniDic (unidic-mecab) dictionary/statiscal models are packaged in Kagome binary. Kagome has segmentation mode for search such as Kuromoji.
The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.
Think of the ExtensionsIndex as a repository containing a list of extension description files (*.s4ext) used by the Slicer extensions build system to build, test, package and upload extensions on an extensions server. Once uploaded on an extensions server, within Slicer, extensions can be installed using the extensions manager.
However this code includes radical differences (such as data loading, augmentation, memory optimization) and it has more generic type of implementation suitable for use in any custom project. You only need to modify data-loader files data/custom-gen.lua and data/custom.lua. Be warned this is susceptible to bugs. Any pull request is appreciated.
Natural language processing algorithms implemented in pure Ruby with minimal dependencies. NOTE: this is not affiliated with, endorsed by, or in any way connected with Pure NLP, a trademark of John La Valle.
This repository contains code to train state-of-the-art cardiac segmentation networks as described in this paper: An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation. The modified U-Net architecture achieved the 3rd overall rank at the MICCAI 2017 ACDC Cardiac segmentation challenge. Create an environment with Python 3.4. If you use virutalenv it might be necessary to first upgrade pip (pip install --upgrade pip).
Efficient implementation of structured sparsity inducing attention mechanisms: fusedmax, oscarmax and sparsemax. Currently available for pytorch v0.2. Requires python (3.6, 3.5, or 2.7), cython, numpy, scipy, scikit-learn, and lightning.
RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars. Have a look at this OpenStreetMap diary post where we first introduced RoboSat and show some results.