Search Results for "segment image"
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Showing 16 open source projects for "segment image"
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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. ... -
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X-AnyLabeling
Effortless data labeling with AI support from Segment Anything
X-AnyLabeling is an open-source data annotation platform designed to streamline the process of labeling datasets for computer vision and multimodal AI applications. The software integrates an AI-powered labeling engine that allows users to generate annotations automatically with the assistance of modern vision models such as Segment Anything and various object detection frameworks. It supports labeling tasks across images and videos and enables developers to prepare training datasets for... -
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an... -
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SAM 3D Objects
Models for object and human mesh reconstruction
SAM 3D Objects is a foundation model that reconstructs full 3D geometry, texture, and spatial layout of objects and scenes from a single image. Given one RGB image and object masks (for example, from the Segment Anything family), it can generate a textured 3D mesh for each object, including pose and approximate scene layout. The model is specifically designed to be robust in real-world images with clutter, occlusions, small objects, and unusual viewpoints, where many earlier 3D-from-image systems struggle. ... -
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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... -
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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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SAM 2
The repository provides code for running inference with SAM 2
SAM2 is a next-generation version of the Segment Anything Model (SAM), designed to improve performance, generalization, and efficiency in promptable image segmentation tasks. It retains the core promptable interface—accepting points, boxes, or masks—but incorporates architectural and training enhancements to produce higher-fidelity masks, better boundary adherence, and robustness to complex scenes. -
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Easy DataSet
A powerful tool for creating datasets for LLM fine-tuning
Easy DataSet is a comprehensive open-source tool designed to make creating high-quality datasets for large language model fine-tuning, retrieval-augmented generation (RAG), and evaluation as easy and automated as possible by providing intuitive interfaces and powerful parsing, segmentation, and labeling tools. It supports ingesting domain-specific documents in a wide range of formats — including PDF, Markdown, DOCX, EPUB, and plain text — and can intelligently segment, clean, and structure... -
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Mesh R-CNN
code for Mesh R-CNN, ICCV 2019
...The system combines 2D detection from Mask R-CNN with 3D reasoning modules that output full mesh reconstructions aligned with the input image. It has been evaluated on datasets such as Pix3D, where it demonstrates state-of-the-art performance in reconstructing real-world object geometry. -
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AnyTXT Searcher
A Powerful Desktop Full-Text Search Engine, Just Like Local Google.
AnyTXT Searcher is a powerful file full-text search engine, a desktop search application for fast document retrieval. Just like a local disk Google search engine, much faster than Windows Search, it is your ideal desktop file content full-text search engine. It has a powerful document parsing engine built in, which extracts the text of commonly used file formats without installing any other software, and combines the built-in high-speed indexing system to store the metadata of the... -
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Network Security Toolkit (NST)
A network security analysis and monitoring toolkit Linux distribution.
Network Security Toolkit (NST) is a bootable ISO image (Live USB Flash Drive) based on Fedora 42 providing easy access to best-of-breed Open Source Network Security Applications and should run on most x86_64 systems. The main intent of developing this toolkit was to provide the security professional and network administrator with a comprehensive set of Open Source Network Security Tools. The majority of tools published in the article: Top 125 Security Tools by INSECURE.ORG are available... -
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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. -
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DSeg software
A MATLAB program to segment filamentous bacteria and hyphae structures
The analysis of microscopy image has been the basis to our current understanding of the cellular growth and morphogenesis. The quantitative evaluation of morphological changes in the biological processes is therefore important to characterize cell structures. Here we present an image analysis tool DSeg to overcome the difficulties in finding complicated elongated cell shapes by using time-lapse data and cell morphological constraints. A fast binary level-set based algorithm is implemented... -
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DeepMask
Torch implementation of DeepMask and SharpMask
DeepMask is an early, influential approach to class-agnostic object segmentation that learns to propose pixel-accurate masks directly from images. Instead of first generating boxes and then refining them, the network predicts a foreground mask and an “objectness” score for a given image patch, yielding high-quality segment proposals suitable for downstream detection or instance segmentation. The model is trained end-to-end to align mask shape with object extent, which markedly improves recall at a manageable number of proposals. In practice, DeepMask is run on an image pyramid with a sliding window, followed by non-maximum suppression to produce a compact set of candidates. ... -
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CodaChrome
CodaChrome: The Pan-Genomic Explorer
CodaChrome is a tool for visually exploring how the proteome of a bacterium of interest relates to every other finished bacterial genome in GenBank. -
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graphseg
Library for segmenting a graph, images in particular.
Primarily this library was developed for producing a set of super-pixels from an image (i.e. an over-segmentation). The segmentation is produced by running multiple instances of Dijkstra's Algorithm from a set of seed points; those nodes which belong to a given Dijkstra cloud form a segment (so the number of segments is fixed and determined at the beginning of the algorithm). The goal is to add code that moves from the over-segmentation to a more useful segmentation. ...
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