Search Results for "fast linux"
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Showing 24 open source projects for "fast linux"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Segment Anything
Provides code for running inference with the SegmentAnything Model
Segment Anything (SAM) is a foundation model for image segmentation that’s designed to work “out of the box” on a wide variety of images without task-specific fine-tuning. It’s a promptable segmenter: you guide it with points, boxes, or rough masks, and it predicts high-quality object masks consistent with the prompt. The architecture separates a powerful image encoder from a lightweight mask decoder, so the heavy vision work can be computed once and the interactive part stays fast. A... -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection.... -
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MetaCLIP
ICLR2024 Spotlight: curation/training code, metadata, distribution
MetaCLIP is a research codebase that extends the CLIP framework into a meta-learning / continual learning regime, aiming to adapt CLIP-style models to new tasks or domains efficiently. The goal is to preserve CLIP’s strong zero-shot transfer capability while enabling fast adaptation to domain shifts or novel class sets with minimal data and without catastrophic forgetting. The repository provides training logic, adaptation strategies (e.g. prompt tuning, adapter modules), and evaluation... -
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MIVisionX
Set of comprehensive computer vision & machine intelligence libraries
MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning... -
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fastai
Deep learning library
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying... -
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Hiera
A fast, powerful, and simple hierarchical vision transformer
Hiera is a hierarchical vision transformer designed to be fast, simple, and strong across image and video recognition tasks. The core idea is to use straightforward hierarchical attention with a minimal set of architectural “bells and whistles,” achieving competitive or superior accuracy while being markedly faster at inference and often faster to train. The repository provides installation options (from source or Torch Hub), a model zoo with pre-trained checkpoints, and code for evaluation... -
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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:... -
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Blazeface
Blazeface is a lightweight model that detects faces in images
Blazeface is a lightweight, high-performance face detection model designed for mobile and embedded devices, developed by TensorFlow. It is optimized for real-time face detection tasks and runs efficiently on mobile CPUs, ensuring minimal latency and power consumption. Blazeface is based on a fast architecture and uses deep learning techniques to detect faces with high accuracy, even in challenging conditions. It supports multiple face detection in varying lighting and poses, and is designed... -
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PyCls
Codebase for Image Classification Research, written in PyTorch
pycls is a focused PyTorch codebase for image classification research that emphasizes reproducibility and strong, transparent baselines. It popularized families like RegNet and supports classic architectures (ResNet, ResNeXt) with clean implementations and consistent training recipes. The repository includes highly tuned schedules, augmentations, and regularization settings that make it straightforward to match reported accuracy without guesswork. Distributed training and mixed precision are... -
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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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Gluon CV Toolkit
Gluon CV Toolkit
GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection,... -
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PyArmadillo
linear algebra library for Python
PyArmadillo - streamlined linear algebra library for Python, with emphasis on ease of use. Alternative to NumPy / SciPy. * Main page: https://pyarma.sourceforge.io * Documentation: https://pyarma.sourceforge.io/docs.html * Bug reports: https://pyarma.sourceforge.io/faq.html * Git repo: https://gitlab.com/jason-rumengan/pyarma -
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DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based... -
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The Integrating Vision Toolkit (IVT) is a powerful and fast C++ computer vision library with an easy-to-use object-oriented architecture. It offers its own multi-platform GUI toolkit. OpenCV is integrated optionally. Website: http://ivt.sourceforge.net
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maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation
Mask R-CNN Benchmark is a PyTorch-based framework that provides high-performance implementations of object detection, instance segmentation, and keypoint detection models. Originally built to benchmark Mask R-CNN and related models, it offers a clean, modular design to train and evaluate detection systems efficiently on standard datasets like COCO. The framework integrates critical components—region proposal networks (RPNs), RoIAlign layers, mask heads, and backbone architectures such as... -
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Butteraugli
Estimates the psychovisual difference between two images
butteraugli is a perceptual similarity metric designed to estimate how noticeable differences between two images will be to the human eye. Instead of simple pixel math, it models aspects of human vision—color sensitivity, spatial masking, and contrast perception—to highlight differences that viewers actually see. The core tool outputs a single “distance” score along with per-pixel or per-region maps that show where artifacts are most objectionable. These maps make it practical to tune... -
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Faster R-CNN
Object detection framework based on deep convolutional networks
This repository provides a MATLAB / Caffe re-implementation of the Faster R-CNN object detection framework (originally from Ren et al. 2015). The Faster R-CNN architecture combines a Region Proposal Network (RPN) with a Fast R-CNN style detection network to share convolutional feature maps and thus speed up detection. The repo includes code to train, test, and deploy Faster R-CNN models under the MATLAB / Caffe environment, example configuration files, and model checkpoints. Multiple... -
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Accord.NET Framework
Machine learning, computer vision, statistics and computing for .NET
...It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and extensive documentation and a wiki help fill in the details. The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile. ... -
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R-FCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN (“Region-based Fully Convolutional Networks”) is an object detection framework that makes almost all computation fully convolutional and shared across the image, unlike prior region-based approaches (e.g. Faster R-CNN) which run per-region sub-networks. The repository provides an implementation (in Python) supporting end-to-end training and inference of R-FCN models on standard datasets. The authors propose position-sensitive score maps to reconcile the need for translation variance... -
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Low-power approximate adders provide basic building blocks for approximate computing hardware that have shown remarkable energy efficiency for error-resilient applications (like image/video processing, computer vision, etc.), especially for battery-driven portable systems. In this paper, we present a novel scalable, fast yet accurate analytical method to evaluate the output error probability of multi-bit low power adders for a predetermined probability of input bits. Our method recursively...
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HashingBaselineForImageRetrieval
Various hashing methods for image retrieval and serves as the baseline
This repository provides baseline implementations of deep supervised hashing methods for image retrieval tasks using PyTorch. It includes clean, minimal code for several hashing algorithms designed to map images into compact binary codes while preserving similarity in feature space, enabling fast and scalable retrieval from large image datasets. -
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reacTIVision is a computer vision framework for the fast and robust tracking of markers attached on physical objects, and the creation of multi-touch surfaces. It was designed for the rapid development of table-based tangible user interfaces.
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Edges
Structured Edge Detection Toolbox
Structured Edge Detection (Edges) is a MATLAB toolbox implementing the structured forests method for fast and accurate edge detection (up to ~60 fps in many settings). The toolbox also includes the Edge Boxes object proposal method, fast superpixel generation, and utilities for training, evaluation, and integration with vision pipelines. High performance (frames per second performance depending on settings). Integration with MATLAB and compatibility with external vision pipelines. Fast edge... -
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Gandalf is a computer vision and numerical algorithm library, written in C, which allows you to develop new applications that will be portable and run FAST. Dynamically reconfigurable vector, matrix and image structures allow efficient use of memory.
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