Search Results for "recognition"
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Showing 27 open source projects for "recognition"
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OpenVINO
OpenVINO™ Toolkit repository
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more. Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. ... -
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OpenCV
Open Source Computer Vision Library
OpenCV (Open Source Computer Vision Library) is a comprehensive open-source library for computer vision, machine learning, and image processing. It enables developers to build real-time vision applications ranging from facial recognition to object tracking. OpenCV supports a wide range of programming languages including C++, Python, and Java, and is optimized for both CPU and GPU operations. -
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Computer Vision Annotation Tool (CVAT)
Interactive video and image annotation tool for computer vision
Computer Vision Annotation Tool (CVAT) is a free and open source, interactive online tool for annotating videos and images for Computer Vision algorithms. It offers many powerful features, including automatic annotation using deep learning models, interpolation of bounding boxes between key frames, LDAP and more. It is being used by its own professional data annotation team to annotate millions of objects with different properties. The UX and UI were also specially developed by the team for... -
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Diffgram
Training data (data labeling, annotation, workflow) for all data types
...Annotation is required because raw media is considered to be unstructured and not usable without it. That’s why training data is required for many modern machine learning use cases including computer vision, natural language processing and speech recognition. -
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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: http://arma.sourceforge.net/download.html * Documentation: http://arma.sourceforge.net/docs.html * Bug reports: http://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/conradsnicta/armadillo-code -
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OculiX
Visual Automation IDE — automate anything you see on screen
OculiX is the evolution of SikuliX, actively maintained with the full agreement of its original creator RaiMan. Automate any desktop application using image recognition (OpenCV) and OCR (Tesseract + PaddleOCR). No access to source code or DOM required — if you can see it, you can automate it. Key features: - Guided step-by-step recorder with live code preview - Image recognition via OpenCV 4.10 - Dual OCR: Tesseract (built-in) + PaddleOCR (neural, high precision) - Local and remote automation via integrated VNC - SSH tunnels via embedded JSch - Cross-platform: Windows, macOS (Apple Silicon M1-M4), Linux - Scripting: Jython, JRuby, Java, PowerShell, AppleScript - Java 17 recommended (Java 8+ supported) - Full CI/CD with automated builds for all platforms Used worldwide for test automation, RPA, and visual regression testing. ... -
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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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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 and fine-tuning on standard benchmarks. ... -
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ImageAI
A python library built to empower developers
ImageAI is an easy-to-use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find features supported, links to official documentation as well as articles on ImageAI. ImageAI is widely used around the world by professionals,... -
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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Hello AI World
Guide to deploying deep-learning inference networks
...In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. The tutorial focuses on networks related to computer vision, and includes the use of live cameras. You’ll also get to code your own easy-to-follow recognition program in Python or C++, and train your own DNN models onboard Jetson with PyTorch. Ready to dive into deep learning? It only takes two days. We’ll provide you with all the tools you need, including easy to follow guides, software samples such as TensorRT code, and even pre-trained network models including ImageNet and DetectNet examples. ... -
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ConvNeXt
Code release for ConvNeXt model
...It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure makes it efficient for both pretraining and fine-tuning across a wide range of visual recognition tasks. It achieves competitive or superior results on ImageNet and downstream datasets while being easier to deploy and train than transformers. The repository provides pretrained models, training recipes, and ablation studies demonstrating how incremental design choices collectively yield state-of-the-art performance. -
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Gluon CV Toolkit
Gluon CV Toolkit
...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, semantic segmentation and pose estimation, to instance segmentation and video action recognition. The model zoo is the one-stop shopping center for many models you are expecting. GluonCV embraces a flexible development pattern while is super easy to optimize and deploy without retaining a heavyweight deep learning framework. -
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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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Computer Vision Pretrained Models
A collection of computer vision pre-trained models
...A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self-learning car. You can spend years building a decent image recognition algorithm from scratch or you can take the inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. ... -
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opencv4nodejs
Nodejs bindings to OpenCV 3 and OpenCV 4
OpenCV4NodeJS is a Node.js binding for OpenCV, allowing developers to integrate computer vision capabilities directly into JavaScript applications for image processing, object detection, and facial recognition. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
...At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
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Deep Learning Drizzle
Drench yourself in Deep Learning, Reinforcement Learning
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures! Optimization courses which form the foundation for ML, DL, RL. Computer Vision courses which are DL & ML heavy. Speech recognition courses which are DL heavy. Structured Courses on Geometric, Graph Neural Networks. Section on Autonomous Vehicles. Section on Computer Graphics with ML/DL focus. -
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include DS-GA 1001 Intro to Data Science or a graduate-level machine learning course. To be able to follow the exercises, you are going to need a laptop with Miniconda (a minimal version of Anaconda) and several Python packages installed. The following instruction would work as is for Mac or Ubuntu Linux users, Windows users would need to install and work in the Git BASH terminal. ... -
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OpenFace
A state-of-the-art facial behavior analysis toolkit
...The OpenFace toolkit is capable of performing several complex facial analysis tasks, including facial landmark detection, eye-gaze estimation, head pose estimation and facial action unit recognition. OpenFace is able to deliver state-of-the-art results in all of these mentioned tasks. OpenFace is available for Windows, Ubuntu and macOS installations. It is capable of real-time performance and does not need to run on any specialist hardware, a simple webcam will suffice. -
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OpenPR stands for Open Pattern Recognition project and is intended to be an open source library for algorithms of image processing, computer vision, natural language processing, pattern recognition, machine learning and the related fields.
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Netvlad
NetVLAD: CNN architecture for weakly supervised place recognition
NetVLAD is a deep learning-based image descriptor framework developed by Relja Arandjelović for place recognition and image retrieval. It extends standard CNNs with a trainable VLAD (Vector of Locally Aggregated Descriptors) layer to create compact, robust global descriptors from image features. This implementation includes training code and pretrained models using the Pittsburgh and Tokyo datasets. -
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Awesome Recurrent Neural Networks
A curated list of resources dedicated to RNN
A curated list of resources dedicated to recurrent neural networks (closely related to deep learning). Provides a wide range of works and resources such as a Recurrent Neural Network Tutorial, a Sequence-to-Sequence Model Tutorial, Tutorials by nlintz, Notebook examples by aymericdamien, Scikit Flow (skflow) - Simplified Scikit-learn like Interface for TensorFlow, Keras (Tensorflow / Theano)-based modular deep learning library similar to Torch, char-rnn-tensorflow by sherjilozair, char-rnn... -
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ProximityForest
Efficient Approximate Nearest Neighbors for General Metric Spaces
..., WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer Vision and Pattern Recognition, 2012. This source code is provided without warranty and is available under the GPL license. More commercially-friendly licenses may be available. Please contact Stephen O'Hara for license options. Please view the wiki on this site for installation instructions and examples on reproducing the results of the papers. -
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Time Adaptive Self-Organizing Map
An Artificial Neural Network for Clustering, Classification, etc
This project tries to include Time Adaptive Self-Organizing Map (TASOM) implementations for solving Computational Intelligence problems such as Pattern Recognition, Computer Vision, Clustering, Active Contour Modeling, and the like. The TASOM has been originally introduced for adaptive and changing environments. Several versions of TASOM networks have been introduced. Some of them are capable of changing the number of neurons based on the problems at hand. Moreover, a binary tree version of the TASOM has been introduced for faster performance.
