Search Results for "segmentation"
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Showing 51 open source projects for "segmentation"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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gse
Go efficient multilingual NLP and text segmentation
Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others. Gse is implements jieba by golang, and try add NLP support and more feature. Support common, search engine, full mode, precise mode and HMM mode multiple word segmentation modes. Support user and embed dictionary, Part-of-speech/POS tagging, analyze segment info, stop and trim words. -
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ComfyUI Essentials
Essential nodes that are weirdly missing from ComfyUI core
ComfyUI_essentials is a ComfyUI custom node collection that adds practical nodes the author considers missing from the ComfyUI core. The project focuses on useful workflow building blocks rather than generic duplicates, with nodes for image handling, mask processing, sampling, segmentation, conditioning, text, and miscellaneous operations. Its image tools include functions for batching, cropping, flipping, resizing, compositing, background removal, color matching, LUT application, sharpening, tiling, and latent previewing. Its mask tools include blur, smoothing, fixing, flipping, color-based masks, segmentation masks, bounding boxes, transition masks, and batch utilities. ... -
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MatImage
Image Processing library for Matlab
matImage is an open-source MATLAB library for image processing and analysis. It provides a variety of tools for image enhancement, segmentation, and feature extraction. It’s especially useful for users working on biomedical images or those needing detailed image analysis in MATLAB. -
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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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CO3D (Common Objects in 3D)
Tooling for the Common Objects In 3D dataset
...It builds upon the original CO3Dv1 dataset, expanding both scale and quality—featuring 2× more sequences and 4× more frames, with improved image fidelity, more accurate segmentation masks, and enhanced annotations for object-centric 3D reconstruction. CO3Dv2 enables research in multi-view 3D reconstruction, novel view synthesis, and geometry-aware representation learning. Each of the thousands of sequences in CO3Dv2 captures a common object (from categories like cars, chairs, or plants) from multiple real-world viewpoints. ... -
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BlenderProc
Blender pipeline for photorealistic training image generation
...In general, one run of your script first loads or constructs a 3D scene, then sets some camera poses inside this scene and renders different types of images (RGB, distance, semantic segmentation, etc.) for each of those camera poses. Usually, you will run your script multiple times, each time producing a new scene and rendering e.g. 5-20 images from it. With a little more experience, it is also possible to change scenes during a single script call, read here how this is done. As blenderproc runs in blenders separate python environment, debugging your blenderproc script cannot be done in the same way as with any other python script. -
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ncnn
High-performance neural network inference framework for mobile
...It is cross-platform and supports most commonly used CNN networks, including Classical CNN (VGG AlexNet GoogleNet Inception), Face Detection (MTCNN RetinaFace), Segmentation (FCN PSPNet UNet YOLACT), and more. ncnn is currently being used in a number of Tencent applications, namely: QQ, Qzone, WeChat, and Pitu. -
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UCO3D
Uncommon Objects in 3D dataset
uCO3D is a large-scale 3D vision dataset and toolkit centered on turn-table videos of everyday objects drawn from the LVIS taxonomy. It provides about 170,000 full videos per object instance rather than still frames, along with per-video annotations including object masks, calibrated camera poses, and multiple flavors of point clouds. Each sequence also ships with a precomputed 3D Gaussian Splat reconstruction, enabling fast, differentiable rendering workflows and modern implicit/point-based... -
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GrowthBook
Open source feature flagging and AB testing platform
GrowthBook is an open-source platform for feature flagging and AB testing built to give teams the power of a fully-featured experimentation system without building it entirely from scratch. It supports both self-hosted and cloud-hosted deployment models, giving organizations the flexibility to own their infrastructure or consume it as a managed service. The platform is designed for performance and scale: its SDKs are lightweight, supporting local evaluation to minimize latency, and it... -
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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spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with... -
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fvcore
Collection of common code shared among different research projects
fvcore is a lightweight utility library that factors out common performance-minded components used across Facebook/Meta computer-vision codebases. It provides numerics and loss layers (e.g., focal loss, smooth-L1, IoU/GIoU) implemented for speed and clarity, along with initialization helpers and normalization layers for building PyTorch models. Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code. A... -
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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... -
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Jittor
Jittor is a high-performance deep learning framework
...It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. ... -
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The MITK Workbench is a free, open-source application for medical image visualization, segmentation, registration, and much more. Beyond the Workbench application, MITK is a comprehensive C++ framework for medical image computing. It provides a modular foundation for extending the MITK Workbench with custom plugins or developing your own medical imaging applications and research prototypes.
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Pixelization
Stable-diffusion-webui-pixelization
...It's designed as a plugin you install into the Web UI so that in the “Extras” or “Pixelization” tab you can drag in an input image and produce a stylized, block-based version with control over cell size, color depth, and segmentation. The extension uses pre-trained models and optionally can co-operate with the Web UI’s other features (image-to-image, prompt-based generation) so you can combine pixelization with generative workflows. For digital art, game assets, or retro aesthetic workflows, this offers a fast path from photo or high-res asset to stylized tiles or sprites. ... -
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Unleash
Open-source feature management platform
Unleash is an open-source feature management platform that enables teams to control how and when new features are released in their applications. It uses feature flags to decouple deployment from release, allowing developers to ship code to production while selectively enabling functionality for specific users or environments. This approach reduces risk by enabling gradual rollouts, A/B testing, and quick rollbacks if issues arise. Unleash is designed to be flexible and language-agnostic,... -
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BEVFormer
Implementation of BEVFormer, a camera-only framework
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries. ... -
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Official YOLOv7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art
...YOLOv7 introduced training-time improvements that raise accuracy without increasing inference cost, which is why the project became important in real-time detection research. It supports multiple model sizes and related tasks such as object detection and instance segmentation through associated branches or weights. It is useful for researchers, engineers, and developers building detection systems for video, edge devices, robotics, analytics, and industrial vision. -
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Unet
Source code for unet-pytorch, which can train its own model
...Its README notes that U-Net is better suited to datasets with fewer features and shallow visual structures, such as medical image segmentation, rather than complex VOC-style scenes. It is useful for developers and students who want a clear U-Net implementation for segmentation experiments, custom masks, and biomedical-style image analysis. -
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DeepLabv3 Plus
Encoder-Decoder with Atrous Separable Convolution
...The project also supports multi-GPU training, multiple backbones, learning rate schedules with step and cosine options, optimizer selection, and adaptive learning rate behavior based on batch size. It is useful for users who want a stronger semantic segmentation baseline than U-Net for scene-level segmentation tasks. -
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Detectron2
Next-generation platform for object detection and segmentation
...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. ... -
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Nerfies
This is the code for Deformable Neural Radiance Fields
...This lets the system generate photorealistic novel views of nonrigid subjects—faces, bodies, cloth—while preserving fine detail and consistent lighting. The training pipeline handles imperfect captures by modeling camera poses, exposure variations, and background segmentation, producing stable geometry and appearance. A set of utilities manages dataset preparation, pose estimation, and checkpoints so researchers can reproduce results on their own footage. The work sits at the intersection of graphics and vision, showing how learned volumetric rendering can handle human motion without dense markers or studio rigs. -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to... -
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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... -
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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.
