Search Results for "segmentation"
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Showing 123 open source projects for "segmentation"
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1
labelme Image Polygonal Annotation
Image polygonal annotation with Python
...Video annotation. (video annotation). GUI customization (predefined labels / flags, auto-saving, label validation, etc). Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation). Exporting COCO-format dataset for instance segmentation. (instance segmentation). The first time you run labelme, it will create a config file in ~/.labelmerc. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the --config flag. -
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LISA
LISA: Reasoning Segmentation via Large Language Model
LISA is an open-source multimodal AI system designed to enable language models to perform pixel-level reasoning and segmentation tasks on images. The project introduces a framework where a large language model can interpret natural language instructions and produce segmentation masks that highlight relevant regions in an image. Instead of relying solely on predefined object categories, the model is capable of reasoning about complex textual queries and translating them into visual segmentation outputs. ... -
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Sa2VA
Official Repo For "Sa2VA: Marrying SAM2 with LLaVA
Sa2VA is a cutting-edge open-source multi-modal large language model (MLLM) developed by ByteDance that unifies dense segmentation, visual understanding, and language-based reasoning across both images and videos. It merges the segmentation power of a state-of-the-art video segmentation model (based on SAM‑2) with the vision-language reasoning capabilities of a strong LLM backbone (derived from models like InternVL2.5 / Qwen-VL series), yielding a system that can answer questions about visual content, perform referring segmentation, and maintain temporal consistency across frames in video. ... -
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SAM 3
Code for running inference and finetuning with SAM 3 model
...This capability is grounded in a new data engine that automatically annotated over four million unique concepts, producing a massive open-vocabulary segmentation dataset and enabling the model to achieve 75–80% of human performance on the SA-CO benchmark, which itself spans 270K unique concepts. -
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Grounded-Segment-Anything
Marrying Grounding DINO with Segment Anything & Stable Diffusion
Grounded-Segment-Anything is a research-oriented project that combines powerful open-set object detection with pixel-level segmentation and subsequent creative workflows, effectively enabling detection, segmentation, and high-level vision tasks guided by free-form text prompts. The core idea behind the project is to pair Grounding DINO — a zero-shot object detector that can locate objects described by natural language — with Segment Anything Model (SAM), which can produce detailed masks for objects once they are localized. ... -
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CutLER
Code release for Cut and Learn for Unsupervised Object Detection
CutLER is an approach for unsupervised object detection and instance segmentation that trains detectors without human-annotated labels, and the repo also includes VideoCutLER for unsupervised video instance segmentation. The method follows a “Cut-and-LEaRn” recipe: bootstrap object proposals, refine them iteratively, and train detection/segmentation heads to discover objects across diverse datasets. The codebase provides training and inference scripts, model configs, and references to benchmarking results that report large gains over prior unsupervised baselines. ... -
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RF-DETR
RF-DETR is a real-time object detection and segmentation
RF-DETR is an open-source computer vision framework that implements a real-time object detection and instance segmentation model based on transformer architectures. Developed by Roboflow, the project builds upon modern vision transformer backbones such as DINOv2 to achieve strong accuracy while maintaining efficient inference speeds suitable for real-time applications. The model is designed to detect objects and segment them within images or video streams using a unified detection pipeline. ... -
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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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SAM 2
The repository provides code for running inference with SAM 2
...SAM2 comes with pretrained weights and easy-to-use APIs, enabling developers and researchers to integrate promptable segmentation into annotation tools, vision pipelines, or downstream tasks. The project also includes scripts and notebooks to compare SAM2 against SAM on edge cases, benchmarks showing improvements, and evaluation suites to measure mask quality metrics like IoU and boundary error. -
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10
Underthesea
Underthesea - Vietnamese NLP Toolkit
Underthesea is a Vietnamese NLP toolkit providing various text processing capabilities, including word segmentation, part-of-speech tagging, and named entity recognition. -
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KrillinAI
Video translation and dubbing tool powered by LLMs
KrillinAI is an end-to-end content localization, translation, and dubbing tool aimed at helping creators transform videos into multiple languages with minimal manual effort. It integrates several stages of the pipeline: video acquisition (either from local files or remote via download tools), speech recognition (ASR), subtitle segmentation and alignment, machine translation (with context-aware translation to preserve semantics), and voice cloning + text-to-speech (TTS) to produce dubbed audio tracks. KrillinAI supports both landscape and portrait videos, which makes it suitable for a wide range of platforms — from YouTube to TikTok or other vertical-video sites — and ensures correct formatting and layout for the final video. ... -
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pyAudioAnalysis
Python Audio Analysis Library: Feature Extraction, Classification
pyAudioAnalysis is an open-source Python library designed for audio signal analysis, machine learning, and music information retrieval tasks. The project provides a collection of tools that allow developers to extract meaningful features from audio files and use those features for classification, segmentation, and analysis. The library supports multiple audio processing workflows, including feature extraction from raw audio signals, training of machine learning models, and automatic audio segmentation. It also includes utilities for visualizing audio features and analyzing patterns within sound recordings, which can be useful in applications such as speech recognition, music classification, and acoustic event detection. ... -
13
BoxMOT
Pluggable SOTA multi-object tracking modules for segmentation
...It provides a pluggable architecture that allows developers to combine different object detectors with multiple tracking algorithms without modifying the core codebase. 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. -
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DINOv3
Reference PyTorch implementation and models for DINOv3
...The model supports multiple backbone architectures, including Vision Transformers (ViT), and can handle larger image resolutions with improved stability during training. The learned embeddings generalize robustly across tasks like classification, retrieval, and segmentation without fine-tuning, showing state-of-the-art transfer performance among self-supervised models. -
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GeoAI
GeoAI: Artificial Intelligence for Geospatial Data
...It provides a unified framework that combines machine learning libraries such as PyTorch and Transformers with geospatial tools, allowing users to process satellite imagery, aerial photos, and vector datasets in a streamlined workflow. The platform supports a wide range of tasks including image classification, object detection, segmentation, and change detection, making it suitable for applications in environmental monitoring, urban planning, and disaster response. GeoAI simplifies complex workflows by offering high-level APIs that abstract data preprocessing, model training, and inference, reducing the technical barrier for users who are not experts in both AI and geospatial systems. -
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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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HivisionIDPhoto
HivisionIDPhotos: a lightweight and efficient AI ID photos tools
...It also allows the generation of layout sheets such as six-inch photo arrangements for printing multiple ID photos on a single page. The project focuses on building a practical pipeline for automated ID photo production using AI-based segmentation and image processing techniques. -
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The Hypersim Dataset
Photorealistic Synthetic Dataset for Holistic Indoor Scene
...It provides richly annotated renderings—RGB, depth, surface normals, instance and semantic segmentations, and material/lighting metadata—produced from high-fidelity virtual environments. The dataset spans diverse furniture layouts, room types, and camera trajectories, enabling robust training for geometry, segmentation, and SLAM-adjacent tasks. Rendering pipelines and utilities allow researchers to reproduce sequences, generate novel views, or extract task-specific supervision. Because the data are perfectly labeled and controllable, Hypersim is well suited for pretraining and for studying domain transfer to real imagery. The repository acts as both a dataset index and a set of scripts for downloading, managing, and evaluating on standardized splits. -
19
HanLP
Han Language Processing
...Built on TensorFlow 2.0, it was designed to advance state-of-the-art deep learning techniques and popularize the application of natural language processing in both academia and industry. HanLP is capable of lexical analysis (Chinese word segmentation, part-of-speech tagging, named entity recognition), syntax analysis, text classification, and sentiment analysis. It comes with pretrained models for numerous languages including Chinese and English. It offers efficient performance, clear structure and customizable features, with plenty more amazing features to look forward to on the roadmap. -
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Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
...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. -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
...The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints. -
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Segment Anything
Provides code for running inference with the SegmentAnything Model
Segment Anything (SAM) is a foundation model for image segmentation that’s designed to work “out of the box” on a wide variety of images without task-specific fine-tuning. It’s a promptable segmenter: you guide it with points, boxes, or rough masks, and it predicts high-quality object masks consistent with the prompt. The architecture separates a powerful image encoder from a lightweight mask decoder, so the heavy vision work can be computed once and the interactive part stays fast. ... -
23
Ultralytics
Ultralytics YOLO
Ultralytics is a comprehensive computer vision framework that provides state-of-the-art implementations of the YOLO (You Only Look Once) family of models, enabling developers to perform tasks such as object detection, segmentation, classification, tracking, and pose estimation within a unified system. It is designed to be fast, accurate, and easy to use, offering both command-line and Python-based interfaces for training, validation, and deployment of machine learning models. The framework supports a full end-to-end workflow, including dataset preparation, model training, evaluation, and export to various deployment formats. ... -
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imgclsmob Deep learning networks
Sandbox for training deep learning networks
...The project serves as a sandbox for training and evaluating a wide variety of neural network architectures used in image analysis. It includes implementations of models used for tasks such as image classification, object detection, semantic segmentation, and pose estimation. The repository also contains scripts that help train models, evaluate performance, and convert trained networks between different frameworks. Several deep learning frameworks are supported, allowing researchers to experiment with architectures in different environments. The project is frequently used by developers who want to study modern convolutional neural network designs and compare their performance across datasets. -
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SlowFast
Video understanding codebase from FAIR for reproducing video models
...Together, these two pathways complement each other, allowing the network to model both appearance and motion without excessive computational cost. The architecture is modular and supports tasks like action recognition, temporal localization, and video segmentation, performing strongly on benchmarks like Kinetics and AVA. The repository provides training recipes, pretrained models, and distributed pipelines optimized for large-scale video datasets.
