Search Results for "active shape model"
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Showing 115 open source projects for "active shape model"
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ConnectWise CPQ, formerly ConnectWise Sell, is a professional quote and proposal automation software for IT solution providers. ConnectWise CPQ offers a wide range of tools that enables IT solution providers to save time, quote more, and win big. Top features include professional quote or proposal templates, product catalog and sourcing, workflow automation, sales reporting, and integrations with best-in-breed solutions like Cisco, Dell, HP, and Salesforce.
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Vertex AI offers everything you need to build and use generative AI—from AI solutions, to Search and Conversation, to 130+ foundation models, to a unified AI platform.
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Encord Active
The toolkit to test, validate, and evaluate your models and surface
Encord Active is an open-source toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling to supercharge model performance. Encord Active has been designed as a all-in-one open source toolkit for improving your data quality and model performance. Use the intuitive UI to explore your data or access all the functionalities programmatically. Discover errors, outliers, and edge-cases within your data - all in one open source toolkit... -
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ZeroMQ
ZeroMQ core engine in C++, implements ZMTP/3.1
ZeroMQ (also known as ØMQ, 0MQ, or zmq) looks like an embeddable networking library but acts like a concurrency framework. It gives you sockets that carry atomic messages across various transports like in-process, inter-process, TCP, and multicast. You can connect sockets N-to-N with patterns like fan-out, pub-sub, task distribution, and request-reply. It's fast enough to be the fabric for clustered products. Its asynchronous I/O model gives you scalable multicore applications, built... -
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Brim
Application to efficiently search and analyze super-structured data
Desktop application to efficiently search and analyze super-structured data. Powered by Zed. Zed is a system that makes data easier by utilizing our new super-structured data model. Brim is a desktop app to explore, query, and shape the data in your super-structured data lake. Brim is an open source desktop application for security and network specialists. Brim makes it easy to search and analyze data from packet captures, like those created by Wireshark, and structured logs, especially from... -
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HarfBuzz
Open source text shaping engine
HarfBuzz is an open source text-shaping engine with a C API that turns fonts and strings of character codes into a form that is correctly arranged for the corresponding language and writing system. This is essentially the process of text shaping: translating a string of character codes into a properly arranged sequence of glyphs that can be rendered onto a screen or into final output form for inclusion in a document. This shaping depends on a number of factors: the input string, the active font... -
Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech. Dialogflow CX and ES provide virtual agent services for chatbots and contact centers. If you have a contact center that employs human agents, you can use Agent Assist to help your human agents. Agent Assist provides real-time suggestions for human agents while they are in conversations with end-user customers.
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ONNX
Open standard for machine learning interoperability
... learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring). ONNX is widely supported and can be found in many frameworks, tools, and hardware. Enabling interoperability between different frameworks and streamlining the path from research to production helps increase the speed of innovation in the AI community. -
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nichenetr
NicheNet: predict active ligand-target links between interacting cells
nichenetr: the R implementation of the NicheNet method. The goal of NicheNet is to study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest. This model of prior information on potential... -
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eos
A lightweight 3D Morphable Face Model library in modern C++
eos is a lightweight 3D Morphable Face Model fitting library that provides basic functionality to use face models, as well as camera and shape fitting functionality. It's written in modern C++11/14. MorphableModel and PcaModel classes to represent 3DMMs, with basic operations like draw_sample(). Supports the Surrey Face Model (SFM), 4D Face Model (4DFM), Basel Face Model (BFM) 2009 and 2017, and the Liverpool-York Head Model (LYHM) out-of-the-box. -
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directus
Directus wraps any SQL database with a real-time GraphQL+REST API
... to a proprietary data model, so there's no vendor lock-in. Directus is the world's leading Open Data Platform, with millions of downloads, thousands of active developers, and we're just getting warmed up. Start with a new or existing database, using any SQL vendor, Directus takes care of the rest. The Directus API layers on top of your database, providing both REST and GraphQL APIs for your data. -
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UMAP.jl
Uniform Manifold Approximation and Projection (UMAP) implementation
A pure Julia implementation of the Uniform Manifold Approximation and Projection dimension reduction algorithm. The umap function takes two arguments, X (a column-major matrix of shape (n_features, n_samples)), n_components (the number of dimensions in the output embedding), and various keyword arguments. -
KrakenD is a high-performance API Gateway optimized for resource efficiency, capable of managing 70,000 requests per second on a single instance. The stateless architecture allows for straightforward, linear scalability, eliminating the need for complex coordination or database maintenance.
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MegEngine
Easy-to-use deep learning framework with 3 key features
MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm... -
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Label Sleuth
Open source no-code system for text annotation and building of text
An open-source no-code system for text annotation and building text classifiers. No AI knowledge needed. From task definition to working model in just a few hours! While domain experts label their data, Label Sleuth automatically trains in the background-appropriate machine learning models. To avoid wasted labeling effort, Label Sleuth employs active learning techniques to guide the user in what they should be labeled next. Domain experts can quickly start labeling their data through... -
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Fairlearn
A Python package to assess and improve fairness of ML models
Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms... -
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GemGIS
Spatial data processing for geomodeling
GemGIS is a Python-based, open-source geographic information processing library. 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... -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement... -
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PowerSystems.jl
Data structures in Julia to enable power systems analysis
The PowerSystems.jl package provides a rigorous data model using Julia structures to enable power systems analysis and modeling. In addition to stand-alone system analysis tools and data model building, the PowerSystems.jl package is used as the foundational data container for the PowerSimulations.jl and PowerSimulationsDynamics.jl packages. PowerSystems.jl supports a limited number of data file formats for parsing. -
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Letterboxd Recommendations
Scraping publicly-accessible Letterboxd data for movie recommendations
Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username. A user's "star" ratings are scraped from their Letterboxd profile and assigned numerical ratings from 1 to 10 (accounting for half stars). Their ratings are then combined with a sample of ratings from the top 4000 most active users on the site to create a collaborative filtering recommender model using singular value... -
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Cleanlab
The standard data-centric AI package for data quality and ML
... label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. If you use a sklearn-compatible classifier, all cleanlab methods work out-of-the-box. -
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Kaleidoscope-SDK
User toolkit for analyzing and interfacing with Large Language Models
kaleidoscope-sdk is a Python module used to interact with large language models hosted via the Kaleidoscope service available at: https://github.com/VectorInstitute/kaleidoscope. It provides a simple interface to launch LLMs on an HPC cluster, asking them to perform basic features like text generation, but also retrieve intermediate information from inside the model, such as log probabilities and activations. Users must authenticate using their Vector Institute cluster credentials. This can... -
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Doodle
A pure Kotlin UI framework for the Web and Desktop
Doodle helps you create beautiful, modern apps entirely in Kotlin. Its render model is intuitive yet powerful, making it easy to achieve complex UIs with pixel-level precision and layouts. This simplicity and power apply to everything from user input to drag and drop. Doodle lets you build and animate anything. Start creating your app for the Web and Desktop; just define your View hierarchy and business logic, and go. -
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TypeORM
ORM for TypeScript and JavaScript (ES7, ES6, ES5)
... Active Record and Data Mapper patterns, unlike all other JavaScript ORMs currently in existence, which means you can write high-quality, loosely coupled, scalable, maintainable applications the most productive way. TypeORM is highly influenced by other ORMs, such as Hibernate, Doctrine and Entity Framework. Supports both DataMapper and ActiveRecord (your choice). Uni-directional, bi-directional and self-referenced relations. -
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tsai
Time series Timeseries Deep Learning Machine Learning Pytorch fastai
... it when necessary) We've also added a new PredictionDynamics callback that will display the predictions during training. This is the type of output you would get in a classification task. New tutorial notebook on how to train your model with larger-than-memory datasets in less time achieving up to 100% GPU usage! See our new tutorial notebook on how to track your experiments with Weights & Biases -
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Solid
A declarative JavaScript library for building user interfaces
Solid is a declarative JavaScript library for creating user interfaces. Instead of using a Virtual DOM, it compiles its templates to real DOM nodes and updates them with fine-grained reactions. Declare your state and use it throughout your app, and when a piece of state changes, only the code that depends on it will rerun. Check out our intro video or read on! Fine-grained updates to the real DOM. Declarative data: model your state as a system with reactive primitives. Render-once mental model... -
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LinDB
LinDB is a scalable, high performance, high availability database
LinDB is a scalable, high-performance, high-availability distributed time series database. A single server could easily support more than one million write TPS; With fundamental techniques like efficient compression storage and parallel computing, LinDB delivers highly optimized query performance. The multi-channel replication protocol supports any amount of nodes, and ensures the system's availability. Schema-free multi-dimensional data model with Metric, Tags, and Fields; The LinQL... -
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OGB
Benchmark datasets, data loaders, and evaluators for graph machine
The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. We expect the benchmark datasets to evolve. OGB provides a diverse set of challenging and realistic benchmark datasets... -
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Render Farm Deployment Kit on AWS (RFDK)
Library for use with the AWS Cloud Development Kit
The Render Farm Deployment Kit on AWS (RFDK) is an open-source software development kit (SDK) that can be used to deploy, configure, and manage your render farm infrastructure in the cloud. It offers high-level object-oriented abstractions to define render farm infrastructure using the power of Python and Typescript. The Render Farm Deployment Kit (RFDK) on AWS is an open-source software development kit that can be used to deploy, configure, and manage your render farm infrastructure in the...