Open Source Linux Object Detection Models
Sort By:
Object Detection Models for Linux
View 839 business solutionsBrowse free open source Object Detection Models and projects for Linux below. Use the toggles on the left to filter open source Object Detection Models by OS, license, language, programming language, and project status.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Desktop and Mobile Device Management SoftwareDesktop Central is a unified endpoint management (UEM) solution that helps in managing servers, laptops, desktops, smartphones, and tablets from a central location.
-
1
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. Start training your model without being an expert. Students love YOLOv5 for its simplicity and there are many quickstart examples for you to get started within seconds. Export and deploy your YOLOv5 model with just 1 line of code. There are also loads of quickstart guides and tutorials available to get your model where it needs to be. Create state of the art deep learning models with YOLOv5 -
2
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. We would also like to thank all the people who has helped OpenPose in any way. 15, 18 or 25-keypoint body/foot keypoint estimation, including 6 foot keypoints. Runtime invariant to number of detected people. 2x21-keypoint hand keypoint estimation. Runtime depends on number of detected people. 70-keypoint face keypoint estimation. Runtime depends on number of detected people. Input: Image, video, webcam, Flir/Point Grey, IP camera, and support to add your own custom input source (e.g., depth camera). -
3
Frigate
NVR with realtime local object detection for IP cameras
Frigate - NVR With Realtime Object Detection for IP Cameras A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Use of a Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead. -
4
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. -
Lenso.ai is a perfect example of an AI image search toolLenso.ai is a perfect example of an AI image search tool, where you can simply search for images that you are most interested in. Thanks to advanced AI technology implemented on lenso.ai, you can easily start searching for places, people, duplicates, related or similar images.
-
5
Dlib is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.
-
6
Label Studio
Label Studio is a multi-type data labeling and annotation tool
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. Multi-user labeling sign up and login, when you create an annotation it's tied to your account. 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. -
7
Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
Transformers provides APIs and tools to easily download and train state-of-the-art pre-trained models. Using pre-trained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities. Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. Images, for tasks like image classification, object detection, and segmentation. Audio, for tasks like speech recognition and audio classification. 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. -
8
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 about 1 to 4. The library is tested regularly on MS Windows, Linux, and Mac OS X systems. No other packages are required to use the library, only APIs that are provided by an out of the box OS are needed. There is no installation or configure step needed before you can use the library. All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible. -
9
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, students, research groups and businesses. ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet. ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. -
Accounting practice management softwareCanopy is a cloud-based practice management software for accounting and tax firms, offering tools for client engagement, document management, workflow automation, and time & billing. Its Client Engagement platform centralizes interactions with a secure portal, customizable branding, and email integration, while the Document Management system enables organized, paperless file storage. The Workflow module enhances visibility into tasks and projects through templates, task assignments, and automation, reducing human error. Additionally, the Time & Billing feature tracks billable hours, generates invoices, and processes payments, ensuring accurate financial management. With its comprehensive features, Canopy streamlines operations, reduces stress, and enhances client experiences.
-
10
VoTT
Visual Object Tagging Tool, an electron app for building models
Visual Object Tagging Tool: An electron app for building end-to-end Object Detection Models from Images and Videos. An open source annotation and labeling tool for image and video assets. VoTT is a React + Redux Web application, written in TypeScript. This project was bootstrapped with Create React App. VoTT can be installed as a native application or run from source. VoTT is also available as a stand-alone Web application and can be used in any modern Web browser. VoTT is available for Windows, Linux and OSX. Download the appropriate platform package/installer from GitHub Releases. As noted above, the Web version of VoTT cannot access the local file system; all assets must be imported/exported through a Cloud project. VoTT V2 is a refactor and refresh of the original Electron-based application. As the usage and demand for VoTT grew, V2 was started as an initiative to improve and make VoTT more extensible and maintainable. -
11
Raster Vision
Open source framework for deep learning satellite and aerial imagery
Raster Vision is an open source framework for Python developers building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery). There is built-in support for chip classification, object detection, and semantic segmentation using PyTorch. Raster Vision allows engineers to quickly and repeatably configure pipelines that go through core components of a machine learning workflow: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment. The input to a Raster Vision pipeline is a set of images and training data, optionally with Areas of Interest (AOIs) that describe where the images are labeled. The output of a Raster Vision pipeline is a model bundle that allows you to easily utilize models in various deployment scenarios. -
12
Simd
High performance image processing library in C++
The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM. The Simd Library has C API and also contains useful C++ classes and functions to facilitate access to C API. The library supports dynamic and static linking, 32-bit and 64-bit Windows, Android and Linux, MSVS, G++ and Clang compilers, MSVS project and CMake build systems. -
13
ML.NET
Open source and cross-platform machine learning framework for .NET
With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that automates the process of building best performing models for your Machine Learning scenario. All you have to do is load your data, and AutoML takes care of the rest of the model building process. 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. -
14
MMTracking
OpenMMLab Video Perception Toolbox
MMTracking is an open-source video perception toolbox by PyTorch. It is a part of OpenMMLab project. We are the first open-source toolbox that unifies versatile video perception tasks include video object detection, multiple object tracking, single object tracking and video instance segmentation. We decompose the video perception framework into different components and one can easily construct a customized method by combining different modules. MMTracking interacts with other OpenMMLab projects. It is built upon MMDetection that we can capitalize any detector only through modifying the configs. All operations run on GPUs. The training and inference speeds are faster than or comparable to other implementations. We reproduce state-of-the-art models and some of them even outperform the official implementations. -
15
PyTracking
Visual tracking library based on PyTorch
A general python framework for visual object tracking and video object segmentation, based on PyTorch. Official implementation of the RTS (ECCV 2022), ToMP (CVPR 2022), KeepTrack (ICCV 2021), LWL (ECCV 2020), KYS (ECCV 2020), PrDiMP (CVPR 2020), DiMP (ICCV 2019), and ATOM (CVPR 2019) trackers, including complete training code and trained models. -
16
TNN
Uniform deep learning inference framework for mobile
TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. 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. Everyone is welcome to participate in the collaborative construction to promote the further improvement of the TNN inference framework. -
17
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. 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. Albumentations can work with various deep learning frameworks such as PyTorch and Keras. -
18
COCO Annotator
Web-based image segmentation tool for object detection & localization
COCO Annotator is a web-based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection. It provides many distinct features including the ability to label an image segment (or part of a segment), track object instances, label objects with disconnected visible parts, and efficiently store and export annotations in the well-known COCO format. The annotation process is delivered through an intuitive and customizable interface and provides many tools for creating accurate datasets. Several annotation tools are currently available, with most applications as a desktop installation. Once installed, users can manually define regions in an image and creating a textual description. Generally, objects can be marked by a bounding box, either directly, through a masking tool, or by marking points to define the containing area. COCO Annotator allows users to annotate images using free-form curves. -
19
Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion. -
20
Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection. -
21
Detic
Code release for "Detecting Twenty-thousand Classes
Detic (“Detecting Twenty-thousand Classes using Image-level Supervision”) is a large-vocabulary object detector that scales beyond fully annotated datasets by leveraging image-level labels. It decouples localization from classification, training a strong box localizer on standard detection data while learning classifiers from weak supervision and large image-tag corpora. A shared region proposal backbone feeds a flexible classification head that can expand to tens of thousands of categories without exhaustive box annotations. The system supports zero- or few-shot extension to novel categories via semantic embeddings and class name supervision, making “open-world” detection practical. Built on Detectron2, the repo includes configs, pretrained weights, and conversion tools to mix fully and weakly supervised sources. Detic is especially useful for applications where label space is vast and long-tailed, but dense bounding-box annotation is infeasible. -
22
Fast3R
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Fast3R is Meta AI’s official CVPR 2025 release for “Towards 3D Reconstruction of 1000+ Images in One Forward Pass.” It represents a next-generation feedforward 3D reconstruction model capable of producing dense point clouds and camera poses for hundreds to thousands of images or video frames in a single inference pass—eliminating the need for slow, iterative structure-from-motion pipelines. Built on PyTorch Lightning and extending concepts from DUSt3R and Spann3r, Fast3R unifies multi-view geometry, depth estimation, and camera registration within a single transformer-based architecture. It outputs high-quality 3D scene representations from unordered or sequential views, scaling to large datasets and varied camera intrinsics. The repository includes pretrained models, Gradio-based demos, and modular APIs for direct integration into research or production workflows. -
23
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. At the same time, it is possible to build a video inference loop from scratch using just Norfair and a detector. Supports moving camera, re-identification with appearance embeddings, and n-dimensional object tracking. Norfair provides several predefined distance functions to compare tracked objects and detections. The distance functions can also be defined by the user, enabling the implementation of different tracking strategies. -
24
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. -
25
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.
