Search Results for "time"
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Showing 26 open source projects for "time"
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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Vibes don’t ship, Retool doesVibe coding tools create cool demos, but Retool helps you build software your company can actually use. Generate internal apps that connect directly to your data—deployed in your cloud with enterprise security from day one. Build dashboards, admin panels, and workflows with granular permissions already in place. Stop prototyping and ship on a platform that actually passes security review.
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1
Label Studio
Label Studio is a multi-type data labeling and annotation tool
...Configurable label formats let you customize the visual interface to meet your specific labeling needs. Support for multiple data types including images, audio, text, HTML, time-series, and video. -
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Norfair
Lightweight Python library for adding real-time multi-object tracking
Norfair is a customizable lightweight Python library for real-time multi-object tracking. Using Norfair, you can add tracking capabilities to any detector with just a few lines of code. Any detector expressing its detections as a series of (x, y) coordinates can be used with Norfair. This includes detectors performing tasks such as object or keypoint detection. It can easily be inserted into complex video processing pipelines to add tracking to existing projects. -
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Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
...Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
...Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more. Extras for Catalyst library (Visualization of batch predictions, additional metrics). ... -
Outgrown Windows Task Scheduler?Windows Task Scheduler wasn't built for complex, cross-platform automation. Get a free diagnostic that shows exactly where things are failing and provides remediation recommendations. Interactive HTML report delivered in minutes.
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DeepDetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch
...While the Open Source Deep Learning Server is the core element, with REST API, and multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets. Ready for applications of image tagging, object detection, segmentation, OCR, Audio, Video, Text classification, CSV for tabular data and time series. Neural network templates for the most effective architectures for GPU, CPU, and Embedded devices. Training in a few hours and with small data thanks to 25+ pre-trained models. Full Open Source, with an ecosystem of tools (API clients, video, annotation, ...) Fast Server written in pure C++, a single codebase for Cloud, Desktop & Embedded. -
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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 to work in real-world applications like mobile apps, robotics, and other resource-constrained environments. -
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MobileNetV2
SSD-based object detection model trained on Open Images V4
MobileNetV2 is a highly efficient and lightweight deep learning model designed for mobile and embedded devices. It is based on an inverted residual structure that allows for faster computation and fewer parameters, making it ideal for real-time applications on resource-constrained devices. MobileNetV2 is commonly used for image classification, object detection, and other computer vision tasks, achieving high accuracy while maintaining a small memory footprint. It also supports TensorFlow Lite for mobile device deployment, ensuring that developers can leverage its performance on a wide range of platforms. -
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MoveNet
A CNN model that predicts human joints from RGB images of a person
The MoveNet model is an efficient, real-time human pose estimation system designed for detecting and tracking keypoints of human bodies. It utilizes deep learning to accurately locate 17 key points across the body, providing precise tracking even with fast movements. Optimized for mobile and embedded devices, MoveNet can be integrated into applications for fitness tracking, augmented reality, and interactive systems. -
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MediaPipe Face Detection
Detect faces in an image
The MediaPipe Face Detection model is a high-performance, real-time face detection solution that uses machine learning to identify faces in images and video streams. It is optimized for mobile and embedded platforms, offering fast and accurate face detection while maintaining a small memory footprint. This model supports multiple face detections and is highly efficient, making it suitable for a variety of applications such as augmented reality, user authentication, and facial expression analysis. -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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OpenFieldAI - AI Open Field Test Tracker
OpenFieldAI is an AI based Open Field Test Rodent Tracker
OpenFieldAI use AI-CNN to track rodents movement with pretrained OFAI models , or user could create their own model with YOLOv8 for inferencing. The software generates Centroid graph, Heat map and Line path and a spreadsheet containing all calculated parameters like - Speed - Time in and out of ROI - Distance - Entries/Exits for single/multiple pre-recorded videos or live webcam video. The ROI is assigned automatically in multiple video input , and can be manually given in single input. - For Queries/ Reporting Bugs, contact: kabeermuzammil614@gmail.com - Available on WIndows OS - Software Authorship - Muzammil Kabier and Shamili Mariya Varghese ( Sole Authors ) -
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SOD
An Embedded Computer Vision & Machine Learning Library
...Sobel operator, Otsu's binarization and over 100 image/frame processing & analysis interfaces. Designed for computational efficiency and with a strong focus on real-time applications. SOD includes a comprehensive set of both classic and state-of-the-art deep-neural networks with their pre-trained models. -
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NanoDet-Plus
Lightweight anchor-free object detection model
Super fast and high accuracy lightweight anchor-free object detection model. Real-time on mobile devices. NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss. In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training. -
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Darknet YOLO
Real-Time Object Detection for Windows and Linux
This is YOLO-v3 and v2 for Windows and Linux. YOLO (You only look once) is a state-of-the-art, real-time object detection system of Darknet, an open source neural network framework in C. YOLO is extremely fast and accurate. It uses a single neural network to divide a full image into regions, and then predicts bounding boxes and probabilities for each region. This project is a fork of the original Darknet project. -
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YOLOv3
Object detection architectures and models pretrained on the COCO data
Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models. -
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playqt
GUI version of ffplay for Windows
playqt is a Windows GUI version of the well known ffplay program and has been enhanced with Object Detection capabilities. It can process multiple types of media including real time streams. An integrated camera control feature allows control over the camera parameters as well as automatic network configuration and connection. See the README under the files tab for configuration info. Real time object counting is implemented using YOLO detection algorithm. The program can be used with standard or customized models. ... -
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TNN
Uniform deep learning inference framework for mobile
...The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks. At the same time, it refers to the high performance and good scalability characteristics of the industry's mainstream open source frameworks, and expands the support for X86 and NV GPUs. On the mobile phone, TNN has been used by many applications such as mobile QQ, weishi, and Pitu. As a basic acceleration framework for Tencent Cloud AI, TNN has provided acceleration support for the implementation of many businesses. ... -
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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 Ubuntu 20.04. ... -
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OpenPose
Real-time multi-person keypoint detection library for body, face, etc.
OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) on single images. It is authored by Ginés Hidalgo, Zhe Cao, Tomas Simon, Shih-En Wei, Yaadhav Raaj, Hanbyul Joo, and Yaser Sheikh. It is maintained by Ginés Hidalgo and Yaadhav Raaj. OpenPose would not be possible without the CMU Panoptic Studio dataset. -
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Computer Vision Pretrained Models
A collection of computer vision pre-trained models
...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. TensorFlow implementation of 'YOLO: Real-Time Object Detection', with training and an actual support for real-time running on mobile devices. MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. -
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the... -
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DetectAndTrack
The implementation of an algorithm presented in the CVPR18 paper
DetectAndTrack is the reference implementation for the CVPR 2018 paper “Detect-and-Track: Efficient Pose Estimation in Videos,” focusing on human keypoint detection and tracking across video frames. The system combines per-frame pose detection with a tracking mechanism to maintain identities over time, enabling efficient multi-person pose estimation in video. Code and instructions are organized to replicate paper results and to serve as a starting point for researchers working on pose in video. Although the repo has been archived and is now read-only, its issue tracker and artifacts remain useful for understanding implementation details and experimental settings. ... -
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DIGITS
Deep Learning GPU training system
...DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. DIGITS is available as a free download to the members of the NVIDIA Developer Program. ... -
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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 docs, but jpgs etc are supported. ... -
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...In object tracking, the object is located and the moving object is followed. One of the fundamental steps in many computer based vision systems for object tracking and motion detection is real-time segmentation of moving regions in the image sequences. Segmentation is done in order to detect the object accurately. Usually cameras are used as input sensors, for recording. Front end- MATLAB
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LiveVideo
Real-time Video Analysis Software
...Houari Sabirin, and Munchurl Kim, "Moving object tracking in H.264/AVC bitstream," Lecture Notes in Computer Science, vol. 4577, 2007, pp. 483-492. 2. Wonsang You, M.S. Houari Sabirin, and Munchurl Kim, "Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain," Proceedings of SPIE, vol. 7244, 72440D (February 2009).
