A Semantic Segmentation inference API using the Gluoncv CV toolkit

BMW-Semantic-Segmentation-Inference-API-GPU-CPU This is a repository for a Semantic Segmentation inference API using the Gluoncv CV toolkit. The training GUI (also based on the Gluoncv CV toolkit ) for the Semantic Segmentation workflow will be published soon. A sample inference model is provided with this repository for testing purposes. This repository can be deployed using docker. Prerequisites Ubuntu 18.04 or 20.04 LTS Windows 10 pro with hyper-v enabled and docker desktop NVIDIA Drivers (410.x or higher) Docker CE latest stable release […]

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A Residual-Based StyleGAN Encoder via Iterative Refinement

restyle-encoder Official Implementation of our ReStyle paper for both training and evaluation. ReStyle introduces an iterative refinement mechanism which can be applied over different StyleGAN encoders for solving the StyleGAN inversion task. Different from conventional encoder-based inversion techniques, our residual-based ReStyle scheme incorporates an iterative refinement mechanism to progressively converge to an accurate inversion of real images. For each domain, we show the input image on the left followed by intermediate inversion outputs. Getting Started Prerequisites Linux or macOS NVIDIA […]

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Real-time View Synthesis with Neural Basis Expansion

NeX We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce NeXt-level view-dependent effects—in real time. Unlike traditional MPI that uses a set of simple RGBα planes, our technique models view-dependent effects by instead parameterizing each pixel as a linear combination of basis functions learned from a neural network. Moreover, we propose a hybrid implicit-explicit modeling strategy that improves upon fine detail and produces state-of-the-art results. Our method is evaluated […]

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Swapping Autoencoder for Deep Image Manipulation

swapping-autoencoder-pytorch Official Implementation of Swapping Autoencoder for Deep Image Manipulation (NeurIPS 2020) Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang UC Berkeley and Adobe Research Overview Swapping Autoencoder consists of autoencoding (top) and swapping (bottom) operation.Top: An encoder E embeds an input (Notre-Dame) into two codes. The structure code is a tensor with spatial dimensions; the texture code is a 2048-dimensional vector. Decoding with generator G should produce a realistic image (enforced by […]

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Detector-Free Local Feature Matching with Transformers

LoFTR Code for “LoFTR: Detector-Free Local Feature Matching with Transformers”, CVPR 2021 LoFTR: Detector-Free Local Feature Matching with TransformersJiaming Sun*, Zehong Shen*, Yu’ang Wang*, Hujun Bao, Xiaowei ZhouCVPR 2021 TODO List and ETA The entire codebase for data pre-processing, training and validation is under major refactoring and will be released around June.Please subscribe to this discussion thread if you wish to be notified of the code release.In the meanwhile, discussions about the paper are welcomed in the discussion panel. [x] […]

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3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop

PyMAF This repository contains the code for the following paper: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback LoopHongwen Zhang*, Yating Tian*, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, Zhenan Sun Requirements packages necessary files mesh_downsampling.npz & DensePose UV data Run the following script to fetch mesh_downsampling.npz & DensePose UV data from other repositories. bash fetch_data.sh SMPL model files Fetch preprocessed data from SPIN. Download the pre-trained model and put it into the ./data/pretrained_model directory. After […]

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AOT-GAN for High-Resolution Image Inpainting

AOT-GAN-for-Inpainting AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image InpaintingYanhong Zeng, Jianlong Fu, Hongyang Chao, and Baining Guo. AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image InpaintingYanhong Zeng, Jianlong Fu, Hongyang Chao, and Baining Guo. Citation If any part of our paper and code is helpful to your work,please generously cite and star us :kissing_heart: :kissing_heart: :kissing_heart: ! @inproceedings{yan2021agg, author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining}, title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting}, booktitle […]

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Analyzing, storing and visualizing big data, scientifically

root The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data in a very efficient way. Having the data defined as a set of objects, specialized storage methods are used to get direct access to the separate attributes of the selected objects, without having to touch the bulk of the data. Included are histograming methods in an arbitrary number of dimensions, curve fitting, function evaluation, minimization, graphics and […]

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A Python library for Deep Graph Networks

PyDGN This is a Python library to easily experiment with Deep Graph Networks (DGNs). It provides automatic management of data splitting, loading and the most common experimental settings. It also handles both model selection and risk assessment procedures, by trying many different configurations in parallel (CPU). This repository is built upon the Pytorch Geometric Library, which provides support for data management. If you happen to use or modify this code, please remember to cite our tutorial paper: Bacciu Davide, Errica […]

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Learning Versatile Neural Architectures by Propagating Network Codes

NCP Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang, Ping Luo Introduction This work includes:(1) NAS-Bench-MR, a NAS benchmark built on four challenging datasets under practical training settings for learning task-transferable architectures.(2) An efficient predictor-based algorithm Network Coding Propagation (NCP), which back-propagates the gradients of neural predictors to directly update architecture codes along desired gradient directions for various objectives. This framework is implemented and tested with Ubuntu/Mac OS, CUDA 9.0/10.0, Python 3, Pytorch 1.3-1.6, […]

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