Search Results for "ethical tracking tools"
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Showing 15 open source projects for "ethical tracking tools"
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1
BoxMOT
Pluggable SOTA multi-object tracking modules for segmentation
...The framework supports integration with detection, segmentation, and pose estimation models that produce bounding box outputs. It also includes evaluation tools and benchmarking pipelines that allow researchers to test tracking performance on standard datasets such as MOT17 and MOT20. The system offers different performance modes that balance computational efficiency with tracking accuracy depending on the application requirements. -
2
Python Code Tutorials
The Python Code Tutorials
...The repository covers a wide range of programming topics including cybersecurity, networking, web scraping, machine learning, GUI development, and automation scripts. Each tutorial typically includes complete Python code examples and explanations that demonstrate how to build real tools and applications step by step. Many tutorials focus on practical implementations such as building network scanners, web scraping tools, object detection systems, and automation utilities using Python libraries. The repository is organized into thematic directories that group tutorials by topic, allowing learners to navigate easily between areas such as ethical hacking, multimedia processing, or machine learning. -
3
MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
4
MediaPipe Solutions
Cross-platform, customizable ML solutions
MediaPipe is an open-source framework developed by Google for building cross-platform machine learning pipelines that process audio, video, and other streaming data in real time. The system provides developers with tools and reusable components that allow them to combine multiple machine learning models with preprocessing and postprocessing logic into efficient perception pipelines. These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web browsers, and embedded edge devices. MediaPipe is widely used in computer vision and multimedia applications such as hand tracking, face detection, pose estimation, object recognition, and gesture analysis. ... -
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5
supervision
We write your reusable computer vision tools
We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us. -
6
Aim
An easy-to-use & supercharged open-source experiment tracker
Aim logs all your AI metadata (experiments, prompts, etc) enabling a UI to compare & observe them and SDK to query them programmatically. The Aim standard package comes with all integrations. If you'd like to modify the integration and make it custom, create a new integration package and share with others. Aim is an open-source, self-hosted AI Metadata tracking tool designed to handle 100,000s of tracked metadata sequences. The two most famous AI metadata applications are: experiment... -
7
ZenML
Build portable, production-ready MLOps pipelines
A simple yet powerful open-source framework that scales your MLOps stack with your needs. Set up ZenML in a matter of minutes, and start with all the tools you already use. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code.... -
8
Ploomber
The fastest way to build data pipelines
...The system integrates with common development environments such as Jupyter Notebook, VS Code, and PyCharm, enabling data scientists to continue working with familiar tools while building scalable workflows. Ploomber automatically manages task dependencies and execution order, allowing complex pipelines with multiple stages to run reliably. The framework can deploy pipelines across different computing environments including Kubernetes, Airflow, AWS Batch, and high-performance computing clusters. It also helps teams maintain reproducibility by tracking changes in code and rerunning only outdated pipeline tasks. -
9
MLOps Zoomcamp
Free MLOps course from DataTalks.Club
...The course is designed to teach data scientists and engineers how to move machine learning models from experimentation environments into scalable production services. The repository provides lessons, code examples, and assignments that cover the entire MLOps lifecycle, including model training, experiment tracking, deployment, monitoring, and infrastructure management. Students learn to use widely adopted tools such as MLflow, orchestration frameworks, and cloud platforms to manage machine learning pipelines. The curriculum emphasizes hands-on projects so learners gain practical experience building automated ML pipelines and maintaining deployed models. -
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10
Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph... -
11
MuZero General
A commented and documented implementation of MuZero
...The framework is modular so that users can easily add new environments by defining the game logic and associated hyperparameters. It also includes support for distributed training, GPU acceleration, and monitoring tools for tracking learning progress. -
12
Guild AI
Experiment tracking, ML developer tools
Guild AI is an open-source experiment tracking toolkit designed to bring systematic control to machine learning workflows, enabling users to build better models faster. It automatically captures every detail of training runs as unique experiments, facilitating comprehensive tracking and analysis. Users can compare and analyze runs to deepen their understanding and incrementally improve models. -
13
Automatic cell lineage reconstruction
Automatic segmentation and tracking for 3D time-lapse microscopy
From Amat et al., Nature Methods, 2014*: "The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (1) generality, by reconstructing cell lineages in four-dimensional, terabyte-sized image data of fruit-fly, zebrafish and mouse embryos, acquired with three different types of fluorescence microscopes, (2) scalability, by analyzing advanced stages of development with up to 20,000 cells per time point, at 26,000 cells min-1 on a single computer workstation, and (3) ease of use, by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. ... -
14
Kernel Adaptive Filtering Toolbox
a Matlab benchmarking toolbox for kernel adaptive filtering
...A Matlab benchmarking toolbox for kernel adaptive filtering. Kernel adaptive filtering algorithms are online and adaptive regression algorithms based on kernels. They are suitable for nonlinear filtering, prediction, tracking and nonlinear regression in general. This toolbox includes algorithms, demos, and tools to compare their performance. See the included README file for a list of included algorithms and more details. If you use this toolbox in your research please cite: @inproceedings{vanvaerenbergh2013comparative, author = {Van Vaerenbergh, Steven and Santamar{\'i}a, Ignacio}, booktitle = {2013 IEEE Digital Signal Processing (DSP) Workshop and IEEE Signal Processing Education (SPE)}, title = {A Comparative Study of Kernel Adaptive Filtering Algorithms}, year = {2013}, note = {Software available at \url{https://github.com/steven2358/kafbox/}} } -
15
Bermuda Text-to-Speech
This project includes basic NLP and DSP techniques for Text-to-Speech
See TTS demo at: http://rslp.racai.ro/index.php?page=tts This is an entirely written in JAVA project which includes a set of tools and methods designed to enable Multilingual Text-to-Speech (TTS) synthesis. We currently support English and Romanian but we will soon train more models and make them available for download. If you want to read more about our other NLP and TTS tools check out http://nlptools.racai.ro.
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