Projects sharing features with Segment Everything Everywhere All At Once

ISAT with Segment Anything is a desktop application designed for computer vision data labeling and interactive image segmentation. It provides a semi-automatic annotation suite that accelerates the creation of object masks, polygons, and metadata for training datasets. The application translates user actions into spatial constraints through interactive visual prompting with clicked points and bounding boxes, as well as text-based prompts. It operates via a model-agnostic inference pipeline that interchanges multiple vision and language backbone networks through a unified adapter layer. A desk

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

Describe Anything is a multimodal vision-language framework designed for localized visual analysis and automated dataset annotation. It utilizes a vision-language model to generate detailed, context-aware text descriptions for specific regions within images and videos, triggered by user-defined inputs such as points, boxes, or masks. The system distinguishes itself through its ability to maintain object context across video frames via temporal mask propagation and its support for regional question answering without requiring additional model fine-tuning. It provides an OpenAI-compatible API t

This project is an integrated creative interface for image manipulation that combines generative canvas expansion, object segmentation, and diffusion-based inpainting. It functions as an extension for Stable Diffusion, providing a workflow to isolate specific elements and perform targeted content modifications through prompt-guided synthesis. The tool distinguishes itself by automating the creation of precise selection masks, allowing users to identify and isolate objects by pointing to them rather than manually drawing selections. By chaining segmentation models with generative diffusion pip

Track-Anything is an AI-driven video object segmentation and tracking system. It utilizes the Segment Anything Model to isolate and mask multiple objects across video frames, providing tools for automated mask propagation and background-filling inpainting. The system distinguishes itself through a multi-object segmentation pipeline that can follow several distinct targets simultaneously. It includes a video inpainting utility to remove tracked objects and replace them with synthesized background content, as well as temporal mask refinement to correct tracking drift. The project covers broad

pysot is a computer vision framework designed for single object tracking. It provides a platform for implementing and evaluating algorithms that locate and follow specific target objects across sequences of video frames. The project includes implementations of the SiamRPN architecture for region proposal network based localization and the SiamMask model, which combines tracking with binary mask generation to provide pixel-level segmentation of objects. The framework also contains a visual tracking evaluation toolkit used to measure the accuracy and reliability of tracking algorithms against

mmtracking is a PyTorch video perception framework designed for training and deploying computer vision models that analyze sequential image data. It provides specialized tools for multi-object tracking, video instance segmentation, and a configuration-driven system for managing deep learning models. The project utilizes a deep learning model registry and a configuration-driven pipeline to swap model backbones and detectors without modifying the core codebase. This modular approach allows for the development of custom perception architectures by combining various components and configurations.

ByteTrack is a multi-object tracking framework that implements the ByteTrack algorithm, an ECCV 2022 method designed to recover occluded objects and reduce trajectory fragmentation. The core innovation of the project is its association algorithm, which processes every detection box—including low-confidence ones—by using separate high and low score thresholds, Kalman filter motion prediction, and Hungarian algorithm matching to produce consistent object identities across video frames. The project distinguishes itself by its comprehensive approach to handling occlusions and fragmented trajector

Segment Geospatial is a Python toolkit for isolating geographic features in remote sensing imagery using the Segment Anything Model. It functions as a remote sensing image processor that converts map tiles into georeferenced formats to generate segmentation masks from satellite data. The system enables the extraction of geographic objects through automatic mask generation or manual prompts, such as text descriptions, bounding boxes, and interactive markers. It supports timeseries imagery segmentation to track or identify objects across sequences of images over different dates and provides a g

This project is a structured TensorFlow deep learning curriculum and an interactive machine learning course delivered through Jupyter Notebooks. It serves as a technical guide and model zoo providing reference implementations for neural networks and machine learning algorithms. The curriculum focuses on practical implementations of computer vision, including object detection, semantic segmentation, and style transfer. It also provides tutorials for natural language processing, specifically covering word embeddings and encoder-decoder architectures for sequence modeling. The material covers t

This project is a multi-object tracking framework designed to assign persistent identities to detected bounding boxes across consecutive video frames. It functions as a computer vision tracking algorithm that monitors multiple moving targets in real time by associating detections with consistent labels. The system utilizes a state estimation approach centered on a Kalman filter to predict future object positions and maintain identity during detection gaps. It employs the Hungarian algorithm for optimal data association and calculates intersection over union to match predicted track locations

This project is a PyTorch implementation of a research architecture designed for high-resolution representation learning. It serves as a computer vision framework focused on precise keypoint detection, human pose estimation, and semantic image segmentation. The implementation provides specialized tools for identifying anatomical landmarks on the human body and predicting facial keypoint coordinates to analyze orientation and alignment. It utilizes a system of multi-resolution parallel streams and repeated multi-scale fusion to maintain high-resolution representations throughout the network.

This project is a modular research toolkit designed for developing, training, and evaluating deep learning models for object detection, segmentation, and video instance tracking. It provides a flexible training engine that manages complex neural network execution, including distributed training, custom lifecycle hooks, and weight optimization. The framework is built around a hierarchical configuration system that allows users to define architectures, data pipelines, and training hyperparameters through composable, inheritable files. The project distinguishes itself through its highly modular

This project is a foundation model and research toolkit designed for promptable object segmentation and temporal tracking. It provides a unified framework for isolating specific regions or objects within both static images and dynamic video sequences. The system distinguishes itself through a streaming memory architecture that maintains temporal consistency by storing and retrieving object features across frames. This mechanism allows the model to resolve occlusions and preserve object identity even when targets move out of view or change appearance. By utilizing a shared backbone for both im

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

DeepSORT is a real-time multi-object tracking framework designed to maintain consistent identities of multiple objects across video frames. It integrates deep learning appearance features with motion descriptors to track objects through a sequence of video data. The system uses a deep convolutional neural network to generate high-dimensional visual descriptors for person re-identification. These appearance features are combined with motion estimation via Kalman filtering and solved using the Hungarian algorithm to optimally associate detections with existing tracks. The framework includes ca

Grounded-Segment-Anything is a suite of specialized tools for multimodal visual analysis, text-based segmentation, and generative image editing. It integrates text-to-bounding-box detection and high-precision image segmentation masks to function as a text-based image segmenter and an automated visual labeling tool. The project enables text-driven image editing by identifying objects through natural language to perform inpainting and element replacement. It further extends visual analysis into three dimensions, allowing for 3D human reconstruction and the generation of 3D bounding boxes from t

ImageAI is a Python computer vision library providing a suite of tools for image classification, object detection, and video analytics. It functions as an integrated framework for locating and labeling objects in static images and video streams, utilizing deep learning models for identification and categorization. The project includes a model training toolkit that allows for the creation of custom classifiers and detectors through scratch training or transfer learning. It features a GPU-accelerated inference engine to increase processing speed for vision tasks and includes specialized utiliti

This project is a computer vision pipeline that integrates object detection and tracking to monitor moving objects within video streams. It functions as an end-to-end analytics tool that processes video frames to identify, classify, and maintain the unique identity of objects as they move through a scene. The system utilizes a combination of deep learning inference for detection and motion estimation to ensure temporal continuity. By pairing visual appearance descriptors with predictive motion modeling, it maintains object identities even during temporary occlusions or when spatial overlap is

Roboflow Sports is a sports video analysis system that combines object detection and tracking with bird's-eye field visualization. Its core pipeline detects and tracks players, referees, and balls across video frames, then maps those tracked positions onto a radar-style overhead view of the playing field. The system goes beyond basic detection by localizing field boundaries and key landmarks such as pitch lines and corners, enabling spatial mapping of player positions relative to the field geometry. It classifies detected players by team affiliation through visual feature extraction and clust

LibrePhotos is a self-hosted photo management system designed for organizing, tagging, and searching photos and videos stored on personal servers. It functions as an AI-powered media organizer and a multi-user media server, providing isolated accounts and secure permissions so multiple people can manage private collections on a single system. The project differentiates itself through a semantic image search engine that allows for natural language queries based on deep metadata analysis. It incorporates computer vision to automatically detect faces and identify objects, while utilizing geospat

MobileSAM is a lightweight image segmenter and promptable vision model designed for fast object isolation on resource-constrained hardware. It functions as an automatic image masking tool capable of detecting and isolating distinct objects across an entire image without manual input. The system enables prompt-based object masking using coordinate points or bounding boxes to generate precise masks. It also supports all-object image segmentation through object-aware prompt sampling to identify every distinct object in a scene. To facilitate mobile and edge deployment, the model is compatible w

Genkit is an open-source framework for building AI-powered applications. It provides a unified interface for connecting to hundreds of generative AI models from multiple providers, enabling text, image, audio, and video generation through a single API. The framework structures multi-step AI interactions—including chat, retrieval-augmented generation, tool use, and agentic workflows—as composable, traceable flows with built-in streaming and state management. The framework distinguishes itself through a comprehensive developer toolkit that includes a command-line interface and a local developer

FastSAM is an image segmentation framework that uses convolutional neural networks to isolate visual elements and generate masks for detectable objects within images. It provides a system for both automatic all-object segmentation and promptable image segmentation. The project utilizes an inference-optimized architecture to reduce computational overhead, enabling faster mask generation and real-time visual analysis. It supports the creation of precise masks through various prompt inputs, including points, bounding boxes, and text descriptions. The framework covers broader computer vision cap

MagicQuill is a suite of interactive tools for image segmentation, diffusion-based editing, layered composition, and prompt-guided visual synthesis. It functions as a diffusion model image editor and a layered visual composition tool, enabling the addition, removal, and recoloring of image elements through a combination of sketches and text prompts. The system features a prompt-guided image generator that predicts editing instructions by analyzing user drawings to automatically populate text prompts. It allows for visual style control by swapping generative model weights to shift outputs betw

MedSAM is a deep learning framework designed for automating the segmentation of anatomical structures in 2D and 3D medical imagery. It provides specialized tools for fine-tuning pretrained segmentation weights on custom medical datasets and evaluating the accuracy of those predictions against ground truth labels. The project focuses on adapting the Segment Anything Model architecture for medical use, enabling the isolation of specific anatomical structures through prompt-guided methods such as bounding boxes and point prompts. The system covers a full medical AI workflow, including data engi