Search Results for "data vision"
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Showing 20 open source projects for "data vision"
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
...Albumentations supports different computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation. Albumentations works well with data from different domains: photos, medical images, satellite imagery, manufacturing and industrial applications, Generative Adversarial Networks. Albumentations can work with various deep learning frameworks such as PyTorch and Keras. -
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AKHQ
Kafka GUI for Apache Kafka to manage topics, topics data, etc.
Kafka GUI for Apache Kafka to manage topics, topics data, consumers group, schema registry, connect and more. Enabling your teams to search and explore data in a unified console, while supporting its administration and integration within your ecosystem. Multi-Cluster vision into a central console, available in Multi-Cloud environments. Enabling users to access, search and get insights from your topics, including Live Tail. -
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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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ReductStore
The fastest time series object store for Edge AI
...Capture and access blob data as time series, tailored for edge computing, computer vision, and IoT. ReductStore handles blob data without size limits; your disk capacity is the only boundary. Ensure optimal storage management with FIFO quotas, preventing disk space shortages in real-time. Manage your time-series blob data with ease: annotate, filter, and save AI labels or meta-data. -
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CoreNet
CoreNet: A library for training deep neural networks
CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. Its distributed runtime manages synchronization, load balancing, and mixed-precision computation to maximize throughput while minimizing communication bottlenecks. ... -
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CO3D (Common Objects in 3D)
Tooling for the Common Objects In 3D dataset
CO3Dv2 (Common Objects in 3D, version 2) is a large-scale 3D computer vision dataset and toolkit from Facebook Research designed for training and evaluating category-level 3D reconstruction methods using real-world data. It builds upon the original CO3Dv1 dataset, expanding both scale and quality—featuring 2× more sequences and 4× more frames, with improved image fidelity, more accurate segmentation masks, and enhanced annotations for object-centric 3D reconstruction. ... -
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Covalent
Teradata UI Platform built on Angular Material
Reusable tested tools to build robust Angular Material & Angular applications. Vision: To build an atomic, reusable component platform for Teradata to consume, while collaborating in an open-source model. Covalent is a reusable UI platform from Teradata for building web applications with common standards and tooling. It is based on Angular and Material Design. Visualize your data easily. ECharts is an extensive "tried and true" charting library brought to Angular as Covalent Charts. td-chart-x-axis and td-chart-y-axis elements generate an axis inside a td-chart. ... -
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Uncertainty Baselines
High-quality implementations of standard and SOTA methods
Uncertainty Baselines is a collection of strong, well-documented training pipelines that make it straightforward to evaluate predictive uncertainty in modern machine learning models. Rather than offering toy scripts, it provides end-to-end recipes—data input, model architectures, training loops, evaluation metrics, and logging—so results are comparable across runs and research groups. The library spans canonical modalities and tasks, from image classification and NLP to tabular problems,... -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads:... -
AestheticsPro Medical Spa SoftwareAestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
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iJEPA
Official codebase for I-JEPA
...This objective sidesteps generative pixel losses and avoids heavy negative sampling, producing features that transfer strongly with linear probes and minimal fine-tuning. The design scales naturally with Vision Transformer backbones and flexible masking strategies, and it trains stably at large batch sizes. i-JEPA’s predictions are made in embedding space, which is computationally efficient and better aligned with downstream discrimination tasks. The repository provides training recipes, data pipelines, and evaluation code that clarify which masking patterns and architectural choices matter most. -
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ml-surveys
Survey papers summarizing advances in deep learning, NLP, CV, graphs
...It is particularly useful for researchers, data scientists, or engineers who want conceptual clarity about where a field stands, what problems remain, and what techniques are most established. -
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Flux3D.jl
3D computer vision library in Julia
Flux3D.jl is a 3D vision library, written completely in Julia. This package utilizes Flux.jl and Zygote.jl as its building blocks for training 3D vision models and for supporting differentiation. This package also have support of CUDA GPU acceleration with CUDA.jl. -
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M2SQL
Replicate your Mumps Globals into Relational Data Base
M2SQL is a handy tool that permits to map your existing Mumps Globals in a relational Vision. The mapping process is simple and intuitive and is done only once. The Mapping process will describe the Mumps Globals data as a complex of Applications, Hierarchies, Relations and fields. Once mapping is completed you will able to replicate all the Mumps data into Target Relation database such as SQLITE or MYSQL. Normally you will do the replication process once a day while system is silent. ... -
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
This is a ROS package developed for object detection in camera images. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package, you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. The YOLO packages have been tested under ROS Noetic and... -
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PyTorch SimCLR
PyTorch implementation of SimCLR: A Simple Framework
For quite some time now, we know about the benefits of transfer learning in Computer Vision (CV) applications. Nowadays, pre-trained Deep Convolution Neural Networks (DCNNs) are the first go-to pre-solutions to learn a new task. These large models are trained on huge supervised corpora, like the ImageNet. And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. -
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node-opencv
OpenCV Bindings for node.js
OpenCV bindings for Node.js. OpenCV is the defacto computer vision library - by interfacing with it natively in node, we get powerful real time vision in js. People are using node-opencv to fly control quadrocoptors, detect faces from webcam images and annotate video streams. If you're using it for something cool, I'd love to hear about it! You'll need OpenCV 2.3.1 or newer installed before installing node-opencv. You can use opencv to read in image files. Supported formats are in the OpenCV... -
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tracking.js
A modern approach for Computer Vision on the web
The tracking.js library brings different computer vision algorithms and techniques into the browser environment. By using modern HTML5 specifications, we enable you to do real-time color tracking, face detection and much more, all that with a lightweight core (~7 KB) and intuitive interface. To get started, download the project. This project includes all of the tracking.js examples, source code dependencies you'll need to get started. Unzip the project somewhere on your local drive. The... -
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ProximityForest
Efficient Approximate Nearest Neighbors for General Metric Spaces
A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. -
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QVision: Computer Vision Library for Qt
Computer vision and image processing library for Qt.
This library contains among other things a set of graphical widgets for video output, performance evaluation and augmented reality. The library also provides classes for several data types usually required by computer vision and image processing applications such as vectors, matrices, quaternions and images. Thanks to a large number of wrapper functions these objects can be used with highly efficient functionality from third party libraries such as OpenCV, GNU Scientific Library, Computational Geometry Algorithms Library, Intel's Math Kernel Library and Integrated Performance Primitives, the Octave library, etc... -
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Xuse manages requirements, use cases & other artefacts that drive software design. Xuse focuses on clear documentation & communication. It defines an XML data model for requirements & use cases with XSLT providing multiple derived views: HTML/SVG/PDF. Note there is no GUI for entering requirements, however another project (https://sourceforge.net/projects/xguse/) will provide a GUI.
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