Search Results for "detection"
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Showing 503 open source projects for "detection"
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Python Outlier Detection
A Python toolbox for scalable outlier detection
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly detection. PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. ... -
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Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
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DeltaForce-OBS-Locker
Delta Action OBS Lock Plugin
...It is most appropriate for studying visual detection pipelines, UI capture, and input-simulation concepts in controlled environments. -
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DeviceKit
DeviceKit is a value-type replacement of UIDevice
DeviceKit is a value-type replacement of UIDevice. -
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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Anomalib
An anomaly detection library comprising state-of-the-art algorithms
Anomalib is an open-source deep learning library focused on anomaly detection and localization tasks, collecting state-of-the-art algorithms and tools under one modular framework. It provides implementations of leading anomaly detection methods drawn from current research, as well as a full set of utilities for training, evaluating, benchmarking, and deploying these models on both public and private datasets. -
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YOLOv9
Learning What You Want to Learn Using Programmable Gradient Info
YOLOv9 is the official implementation of the paper “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information.” It is a modern object detection repository focused on improving how deep networks preserve useful information during training. The project introduces Programmable Gradient Information and the GELAN architecture to improve gradient flow, parameter efficiency, and train-from-scratch performance. It provides scripts and model assets for training, testing, and running inference on detection tasks. ... -
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ML.NET
Open source and cross-platform machine learning framework for .NET
...ML.NET has been designed as an extensible platform so that you can consume other popular ML frameworks (TensorFlow, ONNX, Infer.NET, and more) and have access to even more machine learning scenarios, like image classification, object detection, and more. -
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KubeClarity
KubeClarity is a tool for detection and management of vulnerabilities
KubeClarity is a tool for detection and management of Software Bill Of Materials (SBOM) and vulnerabilities of container images and filesystems. It scans both runtime K8s clusters and CI/CD pipelines for enhanced software supply chain security. Effective vulnerability scanning requires an accurate Software Bill Of Materials (SBOM) detection. KubeClarity includes a CLI that can be run locally and especially useful for CI/CD pipelines. -
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Video-subtitle-extractor
A GUI tool for extracting hard-coded subtitle (hardsub) from videos
...(CLI version) No need for users to manually set the subtitle area, the project automatically detects the subtitle area through the text detection model. Filter the text in the non-subtitle area and remove the watermark (station logo) text. -
Your monitoring isn't a stack. It's a pile. Fix that.Replace Datadog, New Relic, and Sentry without adding three more dashboards.
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satellite-image-deep-learning
Resources for deep learning with satellite & aerial imagery
This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. If you find this work useful... -
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MediaPipe
Cross-platform, customizable ML solutions for live and streaming media
MediaPipe offers open-source cross-platform, customizable ML solutions for live and streaming media. Provides segmentation masks for prominent humans in the scene. MediaPipe Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning (ML) to infer the 3D surface geometry, requiring only a single camera input without the need for a dedicated depth sensor. Utilizing lightweight model architectures together with GPU... -
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Random Cut Forest by AWS
An implementation of the Random Cut Forest data structure
This repository contains implementations of the Random Cut Forest (RCF) probabilistic data structure. RCFs were originally developed at Amazon to use in a nonparametric anomaly detection algorithm for streaming data. Later new algorithms based on RCFs were developed for density estimation, imputation, and forecasting. The different directories correspond to equivalent implementations in different languages, and bindings to to those base implementations, using language-specific features for greater flexibility of use. -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
...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. 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. ... -
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rtabmap
RTAB-Map library and standalone application
...When a loop closure hypothesis is accepted, a new constraint is added to the map’s graph, then a graph optimizer minimizes the errors in the map. A memory management approach is used to limit the number of locations used for loop closure detection and graph optimization so that real-time constraints on large-scale environments are always respected. RTAB-Map can be used alone with a handheld Kinect, a stereo camera or a 3D lidar for 6DoF mapping, or on a robot equipped with a laser rangefinder for 3DoF mapping. -
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dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is... -
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RKN Block Checker
Diagnose RKN/TSPU internet blocks layer by layer (DNS, TCP, TLS, HTTP)
RKN Block Checker is a small Python CLI for diagnosing whether the current network connection is affected by Russian RKN or TSPU blocking. It tests blocking behavior layer by layer instead of only reporting that a site is unreachable. The tool can help distinguish DNS poisoning, TCP reset behavior, TLS DPI based on SNI, and ISP stub pages. This makes it useful for developers, system administrators, network researchers, and censorship-measurement workflows. It is designed as a console utility... -
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Anthropic Cybersecurity Skills
754 structured cybersecurity skills for AI agents
Anthropic Cybersecurity Skills is a collection of structured prompts, tools, and workflows designed to enhance the cybersecurity capabilities of AI systems. It focuses on defining reusable “skills” that guide AI models in performing tasks such as vulnerability analysis, threat detection, and security auditing. The project is intended for experimentation and development of AI-assisted cybersecurity workflows, providing templates that can be adapted to different environments. It emphasizes structured interaction patterns, ensuring that outputs are consistent and actionable. The repository also encourages modularity, allowing individual skills to be combined into larger security pipelines. ... -
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AndroidJunkCode
Gradle plugin for Android builds that injects dummy code into APKs
AndroidJunkCode is a Gradle plugin for Android builds that injects "junk" or dummy code into APKs to reduce code similarity between variant packages (like shell or channel apps) and the main app. This helps avoid detection by app stores that flag high-duplication, improving release flexibility for variants or "shaded" packages. Helps avoid app store detection as duplicate or shell apps. Supports configuration for code volume/placement control. Reduces similarity between variant APKs and main app. -
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DocTR
Library for OCR-related tasks powered by Deep Learning
...As such, you can select the architecture used for text detection, and the one for text recognition from the list of available implementations. -
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Impeccable
The design language that makes your AI harness better at design
...It can be installed across several AI coding environments, including Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and others. Impeccable also includes deterministic anti-pattern detection through a CLI and browser extension without requiring an API key. It is most useful for developers and designers who want AI-generated interfaces to feel more polished, intentional, and product-aware. -
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Mobile Detect
Mobile_Detect is a lightweight PHP class for detecting mobile devices
...Because it is pattern-driven and optimized for fast matching, it adds minimal runtime overhead to request handling. By abstracting away the complexity of UA sniffing, Mobile-Detect helps teams gracefully support multi-device clients without duplicating detection logic. -
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trackers
Multi-object tracking algorithms
...Trackers is built for flexibility and benchmarking, allowing users to evaluate performance using standard multi-object tracking metrics and compare algorithms easily. Its architecture emphasizes interoperability so developers can combine their preferred detection models with different trackers without rewriting core logic. -
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Highlight.js
JavaScript syntax highlighter with language auto-detection
Highlight.js is a syntax highlighter written in JavaScript. It works in the browser as well as on the server. It can work with pretty much any markup, doesn’t depend on any other frameworks, and has automatic language detection. Highlight.js supports over 180 languages in the core library. There are also 3rd party language definitions available to support even more languages. We strongly recommend <pre><code> wrapping for code blocks. It's quite semantic and "just works" out of the box with zero fiddling. It is possible to use other HTML elements (or combos), but you may need to pay special attention to preserving linebreaks. ... -
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Immutable.js
Immutable collections for JavaScript
...Immutable data is unchangeable once created, which makes application development so much simpler. There’s no defensive copying, and you get advanced memoization and change detection techniques with simple logic. Persistent data gives you a mutative API, one that doesn’t update data in-place but always produces new and updated data. The data structures that Immutable.js provides include List, Stack, Map, OrderedMap, Set and more. These structures are highly efficient on modern JavaScript VMs, and minimize the need to cache or copy data. -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
...The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on mathematical steps. The repository includes clustering, regression, classification, neural networks, anomaly detection, and other standard ML topics. Does not rely heavily on specialized toolboxes or library shortcuts.
