Search Results for "postprocessing"
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Showing 29 open source projects for "postprocessing"
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MediaPipe Solutions
Cross-platform, customizable ML solutions
MediaPipe is an open-source framework developed by Google for building cross-platform machine learning pipelines that process audio, video, and other streaming data in real time. The system provides developers with tools and reusable components that allow them to combine multiple machine learning models with preprocessing and postprocessing logic into efficient perception pipelines. These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web browsers, and embedded edge devices. MediaPipe is widely used in computer vision and multimedia applications such as hand tracking, face detection, pose estimation, object recognition, and gesture analysis. ... -
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Trixi.jl
Trixi.jl: Adaptive high-order numerical simulations of hyperbolic PDEs
...Therefore, next to having an extensible design with a fast implementation, Trixi.jl is focused on being easy to use for new or inexperienced users, including the installation and postprocessing procedures. -
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SentencePiece
Unsupervised text tokenizer for Neural Network-based text generation
...SentencePiece implements subword units (e.g., byte-pair-encoding (BPE) [Sennrich et al.]) and unigram language model [Kudo.]) with the extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific pre/postprocessing. Purely data driven, sentencePiece trains tokenization and detokenization models from sentences. Pre-tokenization (Moses tokenizer/MeCab/KyTea) is not always required. SentencePiece treats the sentences just as sequences of Unicode characters. There is no language-dependent logic. -
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LiteParse
A fast, helpful, and open-source document parser
...LiteParse supports integration with multiple language models, allowing developers to choose the best balance between accuracy and efficiency. It also includes mechanisms for validation and error handling, ensuring that outputs conform to expected schemas and reducing the need for manual postprocessing. The library is particularly useful for tasks such as data extraction, document processing, and building pipelines that require structured outputs from natural language input. -
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Whisper-WebUI
A Web UI for easy subtitle using whisper model
...It supports multiple input sources including local files, YouTube content, and microphone input, making it versatile for different workflows. Whisper WebUI also includes advanced preprocessing and postprocessing features such as voice activity detection, background music separation, and speaker diarization, enabling more accurate and structured outputs. -
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NeMo Retriever Library
Document content and metadata extraction microservice
NeMo Retriever Library is a scalable microservice framework designed for extracting, structuring, and enriching content from documents to support downstream generative AI applications. It processes various document types by splitting them into components such as text, tables, charts, and images, and then applies OCR and contextual analysis to convert them into structured data formats. The system is built on NVIDIA NIM microservices, enabling high-performance parallel processing and efficient... -
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3DWorld
3D Procedural Game Engine Using OpenGL
...Realtime day/night cycle with weather (rain, snow, hail, wind, lightning). Physically based materials with reflection and refraction. Dynamic shadows, ambient occlusion, up to 1024 dynamic light sources, postprocessing effects. Built-in first person shooter game "smiley killer". -
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GemGIS
Spatial data processing for geomodeling
...It is capable of preprocessing spatial data such as vector data (shape files, geojson files, geopackages,…), raster data (tif, png,…), data obtained from online services (WCS, WMS, WFS) or XML/KML files (soon). Preprocessed data can be stored in a dedicated Data Class to be passed to the geomodeling package GemPy in order to accelerate the model-building process. Postprocessing of model results will allow export from GemPy to geoinformation systems such as QGIS and ArcGIS or to Google Earth for further use. -
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DreamCraft3D
Official implementation of DreamCraft3D
DreamCraft3D is DeepSeek’s generative 3D modeling framework / model family that likely extends their earlier 3D efforts (e.g. Shap-E or Point-E style models) with more capability, control, or expression. The name suggests a “dream crafting” metaphor—users probably supply textual or image prompts and generate 3D assets (point clouds, meshes, scenes). The repository includes model code, inference scripts, sample prompts, and possibly dataset preparation pipelines. It may integrate rendering or... -
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Orion
A machine learning library for detecting anomalies in signals
...We built this to provide one place where users can find the latest and greatest in machine learning and deep learning world including our own innovations. Abstract away from the users the nitty-gritty about preprocessing, finding the best pipeline, and postprocessing. We want to provide a systematic way to evaluate the latest and greatest machine learning methods via our benchmarking effort. Build time series anomaly detection platforms custom to their workflows through our backend database and rest API. A way for machine learning researchers to contribute in a scaffolded way so their innovations are immediately available to the end users. -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging Face Inference Toolkit implements various additional environment variables to simplify your deployment experience. ... -
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qvasp
A flexible toolkit for VASP high-through materials simulations
...Realizing high-throughput screening of materials, qvasp program not only can realize high-throughput material structure model constructing, but also provide high-throughput raw data postprocessing toolkits. 2. Assisting users to easily generate input files and process output files, realizing rapid conversion between graphics and code, and improving work efficiency. Most toolkits in qvasp program are written by Wencai Yi, and some functions are originated from the internet. To avoid disputes, I only provide bash source codes in current conditions, and some Fortran programs only provide binary file. ... -
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GASCAP
GASCAP can evaluate adsorption in high-throughput way
...The pseudoatom and graph Voronoi diagram method are used to figure out the unequal adsorption sites from any substrates, then the coadsorption structure can be built between substrates and any adsorbates in high-throughput way. The universal machine learning interatomic potentials are used to accelerate the adsorption energy calculations, and the postprocessing toolkits contain the functions of analyzing adsorption energy, calculating work function and charge transfer. -------------------------------- Install Method -------------------------------- set environment variable in Linux system: export PATH=/public/home/yiwc/software/GASCAP/v0.6/GASCAP-v0.6:$PATH if there is any incompatibility problem, please fix them with: .... -
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MMDeploy
OpenMMLab Model Deployment Framework
...Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN frameworks. Please read getting_started for the basic usage of MMDeploy. -
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PIFuHD
High-Resolution 3D Human Digitization from A Single Image
...It also uses a two-stage architecture: a coarse global model followed by local refinement patches to capture fine detail, balancing global consistency and local detail. The repo includes training pipelines, dataset loaders (for Multi-POP, etc.), and inference scripts for mesh output including depth maps for postprocessing. To help practical use, there are utilities for normal estimation, texture back-projection, mesh cleanup, and integration with rendering pipelines. -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
SageMaker MXNet Inference Toolkit is an open-source library for serving MXNet models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain MXNet model types and utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). ... -
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VAD
Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM
...Post-processing modules (e.g. smoothing, thresholds). The toolkit supports both MATLAB and Python/TensorFlow components (for feature extraction, classification, postprocessing). Acoustic feature extraction (multi-resolution cochleagram, MRCG). Provided real-world dataset with manual annotations. -
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XPL
Unity Post Processing Stack Library
...The project also includes shader implementations and configurable parameters, giving users fine control over visual output. It is particularly useful for achieving cinematic or stylized aesthetics in games. Overall, X-PostProcessing-Library offers a powerful toolkit for enhancing graphics in Unity applications. -
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OpenHAM
Open source heat, air and moisture transport model
...An open source transport model implementation for heat, air and moisture transport in porous media. Console-based, cross-platform solver with bindings to pre- and postprocessing software. A reference implementation for the EN 15026 standard. -
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dpanalyzer
postprocessing tool for Project Gutenberg Distributed Proofreaders
Specialized tool for PostProcessors of books produced by Project Gutenberg Distributed Proofreaders. Parses the markup structure of a project file out of the formatting rounds; reports about the text structure found, and identifies markup errors. Planned future features: generation of normalized dp output by rejoining split paragraphs and moving around footnotes, renumbering of pages; conversion to basic LaTeX and basic HTML markup for further processing. -
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orbkit (Moved to Github)
A Modular Python Toolbox for Cross-Platform Post-Processing of Quantum
...If you use orbkit in your work, please cite it as follows: Gunter Hermann, Vincent Pohl, Jean Christophe Tremblay, Beate Paulus, Hans-Christian Hege, and Axel Schild, "ORBKIT: A Modular Python Toolbox for Cross-Platform Postprocessing of Quantum Chemical Wavefunction Data", J. Comput. Chem. 2016, DOI: 10.1002/jcc.24358. orbkit's documentation can be found at http://orbkit.github.io/ -
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file2dvd
Convert any video file to DVD
Simple Bash script for converting video files to DVDs. Requires bc, mplayer, ffmpeg, mencoder, dvdauthor and mkisofs -
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yorick
scientific programming language and graphics
Yorick is an interpreted programming language for scientific simulations or calculations, postprocessing or steering large simulation codes, interactive scientific graphics, and reading, writing, or translating large files of numbers. -
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apmsWAPP
Pre- and Postprocessing For Affinity Purification Mass Spectrometry
The reliable detection of protein-protein-interactions by affinity purification mass spectrometry (AP-MS) is a crucial stepping stone for the understanding of biological processes. The main challenge in a typical AP-MS experiment is to separate true interaction proteins from contaminants by contrasting counts of proteins binding to specific baits with counts of negative controls.
