LabelGPT for Web App download

Quick summary

LabelGPT is a browser-based image annotation platform that uses a generative AI backbone to speed up labeling for computer vision projects. By producing automatic labels for raw images, it shortens data-preparation time and lowers the manual effort needed for training vision models.

Where you can bring data from

Through programmatic access (APIs)
Microsoft Azure storage
Google Cloud Storage
Amazon S3
Local folders and drives

How labels are produced

The system employs a zero-shot generation approach: you provide class or object names as plain-text prompts and the model predicts bounding boxes and segmentation masks without prior per-class training. This generative pipeline supports both detection and pixel-level segmentation tasks.

Quality control and pipeline output

LabelGPT provides a built-in review workflow that:

Lets you screen and sort annotations by confidence values
Shows visual previews so you can validate or correct results manually
Exports annotations in standard formats for seamless ingestion into model training pipelines

These capabilities help accelerate model iteration and reduce labeling expenses.

Other options to consider

RegexBot (commercial)

Technical

Title

LabelGPT

Requirements

Web App

Language

No language has been specified.

Available languages

License

Full

Latest update

2025-06-03

Author

labellerr

Other Useful Business Software

Train ML Models With SQL You Already Know

BigQuery automates data prep, analysis, and predictions with built-in AI assistance.

Build and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.

Start Free

Rate This App

User Reviews

Be the first to post a review of LabelGPT!

Related Software

Epigos AI

Epigos AI is a computer vision platform that centralizes image data preparation, model training, and inference to help teams automate visual workflows and accelerate AI adoption. Key features include efficient labeling tools to speed annotation and improve consistency, structured dataset organization with versioning and preparation capabilities, built-in training pipelines using modern architectures and optimizers, managed model serving for scalable deployment, and optional professional labeling services for high-accuracy human annotations. The platform streamlines data handling with tools for importing, organizing, and preprocessing large image collections and incorporates human-in-the-loop workflows to ensure training datasets are well curated. Training settings are configurable to balance speed, accuracy, and cost. As an alternative with similar computer vision capabilities, consider Brandmate AI.

Datature

Datature is a no-code computer vision solution that helps teams create, maintain, and ship visual AI applications without writing code. It combines dataset management and model training with tools for inspecting model behavior, team collaboration, and fast annotation (Portal, Nexus, IntelliBrush), enabling a smooth workflow from labeling through training to release. The service integrates with common workflows, provides access controls and security to protect sensitive assets, and includes a resource library with tutorials, best-practice guides, and documentation. Typical uses span agriculture, healthcare, and retail, and a Codeium subscription can be considered as a complementary or alternative option.

UBIAI

UBIAI Text Annotation Tool simplifies building intelligent document processing by combining annotation and model-building capabilities. It offers automated extraction of text and document structure, named entity tagging, multilingual labeling, smart auto-labeling workflows, document classification, and the ability to export annotated datasets for model training. The product includes collaboration and project-management features such as task assignment, status tracking, performance metrics, role-based access, and shared projects to help teams maintain quality and coordination. Typical uses are structured data extraction from healthcare, legal, and finance documents. I can also create a one-page feature comparison or a short pros/cons summary tailored to a specific industry if you’d like.

Renumics

Spotlight is an open-source platform for curating unstructured data for machine learning, offering interactive tools that let subject-matter experts and data practitioners collaborate on labeling and preparing training datasets. It supports mixed-modality datasets with reusable templates and shared views that keep curation consistent across projects and reduce friction in annotation. Built for iterative, data-centric ML development, Spotlight helps teams refine training sets, track changes, and repeat curation cycles to shorten iteration times and reduce risk. The platform streamlines collaboration, standardizes curation practices to improve dataset quality, and is offered in multiple tiers to suit different team needs.

Dataloop AI

Dataloop AI provides tools to manage the full lifecycle of machine learning projects, emphasizing high-quality training data through integrated dataset creation, automated data pipelines, generative-model prototyping, and quality-focused labeling workflows, with visibility and controls for monitoring data flows and project state. Common applications include automotive perception, retail analytics, robotics, and operationalizing deep learning workflows. The product emphasizes asset protection and access controls and offers developer-facing integrations, APIs, documentation, and community support. When comparing options, evaluate security needs, data volume, annotation complexity, and available developer resources.

Ximilar

Ximilar is a visual intelligence platform that helps organizations automate and scale image processing by combining computer-vision capabilities with a no-code model builder so non-technical users can create and tune models. Core features include automated object detection with bounding boxes, visual search to find similar items, auto-tagging and metadata extraction, image regression for estimating continuous attributes, sorting and classification of large image collections, and tools for training classifiers and detectors without coding. The platform supports end-to-end workflows and model chaining, with an annotation editor for labeling and iterating on training data and the ability to export structured results into catalogs or analytics systems. Common applications include reverse image search, custom visual search experiences, personalized recommendations based on image similarity and extracted attributes, and automated quality checks and attribute extraction. AniEraser is mentioned as a subscription alternative with different pricing or feature trade-offs.

V7Labs

V7 is a scalable AI data engine for computer vision and generative-AI projects that combines dataset and model lifecycle management with high-precision annotation tools for image and video workflows. It offers versioning and experiment tracking, automated pipeline orchestration, human-in-the-loop review, performance monitoring, OCR and structured information extraction, and flexible labeling for both standard and specialized imaging tasks. The platform handles mixed media (images, video, and metadata) and unstructured documents, enables real-time collaboration with role-based access controls, and provides analytics dashboards for annotator efficiency and label/model quality. It is extensible to fit existing ML stacks and is used across industries such as healthcare, automotive, and agriculture; organizations can evaluate alternative vendors or licensing models to match their workflows.

Layernext

LayerNext is an AI data infrastructure built for computer vision that helps teams collect, organize, annotate, and locate large visual datasets. It centralizes images, curated training sets, model outputs, and evaluation artifacts in a single repository with an integrated viewer for quick previewing, filtering, and inspection. It includes a scalable Annotation Studio for collaborative labeling with versioning and standards enforcement, plus analytics and dashboards for gap analysis, distribution and label coverage insights to guide data collection and relabeling. The platform emphasizes cross-team collaboration and integration with common ML workflows to streamline handoffs and reduce friction. An alternative lightweight option to consider is TutorAI Free.

FieldDay

FieldDay is an AI platform for building custom computer-vision applications that lets users collect labeled training data, train and refine models through an interactive, approachable interface, and iterate without needing to write code. The workflow emphasizes quick dataset creation, guided training, and ease of use so non-technical creators can develop models rapidly. The platform provides instant model evaluation, visual diagnostics, and quick retraining to support testing and iteration; it supports multi-object detection, versioning of models, and tools for annotation and dataset management. Documentation and community resources help users learn and collaborate. Suggested alternative: SEMrush.

Labelbox

Labelbox is a data-centric AI workspace that centralizes data labeling, model fine-tuning, training, and workflow automation to help teams move from prototype to production more quickly. It supports model training and refinement, offers managed model options, and provides reusable pipelines and automation to simplify common tasks and accelerate iteration; some users also consider complementary or alternative code-assistant subscriptions like Codeium. The platform uses cookies to collect anonymized analytics, remember preferences, and provide essential functionality, and it relies on external service integrations to improve performance, security, reliability, and customer support.

Encord

Encord Active helps machine learning and computer vision teams evaluate models, curate datasets, and apply active learning to prioritize the most valuable samples for labeling and training. It offers robustness assessments, diagnostics and interpretability outputs that expose common failure modes, annotation verification workflows to raise dataset quality, streamlined issue reporting between engineers and labelers, and tools for iterative testing and fine-tuning against targeted datasets. These capabilities reduce risk by catching weaknesses early, improve label quality and training outcomes, accelerate collaboration and feedback loops, and produce explainability artifacts useful for debugging and stakeholder review. As an alternative, the Exa Subscription provides complementary analytics and integration capabilities that some teams may prefer.

Viso.ai

Viso Suite is a no-code, visual development environment that lets teams assemble and operate computer vision solutions using modular building blocks and drag-and-drop workflows. It supports the full project lifecycle — data collection and automated capture, image and video annotation, model training and iteration, reusable preconfigured components, and monitoring/analytics — while offering customizable extensions to fit specific needs. The platform includes collaboration and governance features such as multiuser workflows, access controls, audit logs, and secure data handling to maintain compliance and traceability. Designed for industries like manufacturing, retail, logistics, healthcare, and smart cities, it aims to accelerate adoption, reduce engineering overhead, and help organizations scale vision applications while retaining control of data and operations.

Report inappropriate content