Search Results for "postprocessing"
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Showing 14 open source projects for "postprocessing"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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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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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... -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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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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SAVA
A Signal Acquisition, Visualization & Analysis software
A Signal Acquisition, Visualization & Analysis software application: * Signals, as in analog sensor data * Acquisition, as in data sampling/capturing/recording * Visualization, as in plotting raw/processed data * Analysis, as in live/post data processing Capabilities: The software has build in support for National Instruments Daq equipment (NI-DAQ) or it can load external source scripts (python), for customizable data sources. Supports data exporting/importing to multiple... -
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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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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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This project consists of two python based parts, ParaPost and TurboVTK, in order to post process Turbomachinery CFD results with ParaView and VTK.
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