Search Results for "gpu process"
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Showing 10 open source projects for "gpu process"
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MESHROOM
3D reconstruction software
...Take advantage of motorized-head file. Easy to integrate in your Renderfarm System. Add specific rules to select the most suitable machines regarding CPU, RAM, GPU requirements of each Node. -
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QtScrcpy
Android real-time display control software
QtScrcpy connects to Android devices via USB (or via TCP/IP) for display and control. It does NOT require the root privileges. It supports three major platforms: GNU/Linux, Windows and MacOS. You can write your own script to map keyboard and mouse actions to touches and clicks of the mobile phone according to your needs. A script for "PUBG mobile" and TikTok mapping is provided by default. Once enabled, you can play the game with your keyboard and mouse as the PC version. or you can use... -
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Anime4KCPP
A high performance anime upscaler
Anime4KCPP provides an optimized bloc97's Anime4K algorithm version 0.9, and it also provides its own CNN algorithm ACNet, it provides a variety of way to use, including preprocessing and real-time playback, it aims to be a high-performance tool to process both image and video. This project is for learning and the exploration task of the algorithm course in SWJTU. Anime4K is a simple high-quality anime upscale algorithm. Version 0.9 does not use any machine learning approaches and can be... -
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ArrayFire
ArrayFire, a general purpose GPU library
ArrayFire is a general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs, GPUs, and other hardware acceleration devices. The library serves users in every technical computing market. Data structures in ArrayFire are smartly managed to avoid costly memory transfers and to take advantage of each performance feature provided by the underlying hardware. The community of ArrayFire developers invites you to build with us if... -
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Robust Video Matting (RVM)
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX
We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and matting quality. Furthermore, we propose a novel training strategy that enforces our network on both matting and segmentation objectives. ... -
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Tdarr V2
Distributed transcode automation using FFmpeg/HandBrake
Tdarr is a distributed media transcoding and automation system designed to manage and optimize large audio and video libraries across multiple machines. It operates through a central server and multiple worker nodes that process files in parallel, leveraging both CPU and GPU resources for efficient transcoding. The system uses FFmpeg and HandBrake to convert media files according to user-defined rules, such as codec standardization, bitrate optimization, or format compatibility. Tdarr also includes advanced features like health checks, plugin-based processing pipelines, and automated scheduling to maintain libraries continuously. ... -
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YouTube-8M
Starter code for working with the YouTube-8M dataset
youtube-8m is Google’s open source starter code and reference implementation for training and evaluating machine learning models on the YouTube-8M dataset, one of the largest video understanding datasets publicly released. The repository provides a complete pipeline for video-level and frame-level modeling using TensorFlow, including data reading, model training, evaluation, and inference. It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured... -
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GPUImage 2
Framework for GPU-accelerated video and image processing
...By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS. -
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Accelerated Feature Extraction Tool
A fast GPU accelerated feature extraction software for speech analysis
A fast feature extraction software tool for speech analysis and processing. It incorporates standard MFCC, PLP, and TRAPS features. The tool is a specially designed to process very large audio data sets. It uses GPU acceleration if compatible GPU available (CUDA as weel as OpenCL, NVIDIA, AMD, and Intel GPUs are supported). CPU SSE intrinsic instruction set is used in cases where no compatible GPU present. The output files are stored in HTK format. The software is developed at Department of Cybernetics at University of West Bohemia in Pilsen. -
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10
Ogljpeg
JPEG decompression accelerated on GPU using OpenGL compute shaders
Ogljpeg is JPEG decompression library. Primary focus of this project is to speed up texture upload process using JPEG compression. For this purpose OpenGL compute shaders are used and result of decompression is OpenGL texture loaded in GPU memory.
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