Self-Hosted Data Labeling Tools

DeepSqueak is a MATLAB application for bioacoustics researchers studying animal vocalizations, including ultrasonic calls from mice and rats. It uses neural networks to detect calls in audio recordings and provides a graphical interface for reviewing detections. The code is open source under the BSD-3-Clause license.

Adala is a Python framework for AI engineers and data scientists who want to run data-labeling and evaluation workflows in their own process. It works with local pandas DataFrames and ground-truth examples, but the documented OpenAI and OpenRouter configurations send task inputs to cloud models. Those connections need internet access and an API key.

Cellpose is local AI software for biologists and microscopy researchers who need to identify individual cells and nuclei in images. It runs on Windows, Linux and macOS, with a graphical interface for automatic segmentation, manual annotation and correcting model results.

Cleanlab is a Python library for data scientists and ML engineers checking training data for errors. It uses model predictions or feature embeddings to identify problems in text, image, audio and tabular datasets.

Doccano is a text annotation tool for people preparing training or evaluation datasets for machine learning. Its browser interface lets teams label text together on their own machine or server.

DeepLabCut is an open-source toolbox for researchers who want to track animal movement and behavior on their own computers without attaching physical markers. It estimates the positions of visible features you choose, with support for animals, humans and objects rather than a fixed set of species or body parts.

Augmentoolkit turns documents into datasets for training an LLM on a particular subject. It's for people building custom models around material such as research papers or fictional lore.

ilastik is a desktop tool for researchers who need to identify, classify or count cells and other objects in experimental images. It runs on Linux, macOS and Windows. Its main draw is interactive training: you mark examples in an image with brush strokes and see predictions as you work, without needing machine learning expertise.

Laminar helps developers inspect AI agent runs, find recurring failures and test fixes. It connects production traces with debugging and evaluation tools.

Segment Anything 2 (SAM 2) is Meta's model for creating object masks in images and videos. It's for developers and researchers building visual applications or annotating footage, with interactive selection and correction.

JoyCaption generates image descriptions for people preparing datasets to train or fine-tune diffusion models. It can caption images on your own GPU, reducing the work of writing training descriptions by hand.

X-AnyLabeling is a desktop annotation workspace for researchers and teams preparing text, image, video, point cloud, and multimodal training datasets. Models generate initial labels, and reviewers correct them in the same workspace before exporting the data. It runs on Windows, Linux, and macOS under the GPL-3.0 open-source license.

Argilla is a data annotation and feedback tool for AI engineers and domain experts building training and evaluation datasets. It gives reviewers a shared interface for checking examples and contributing judgments that inform model development.

TagGUI is a desktop app for creators preparing image datasets for generative AI models. It brings automatic captioning and manual tag editing into one workspace so you can review and correct generated descriptions.

PaddleSeg is an image segmentation toolkit for developers building computer vision applications on their own hardware. It covers annotation, training and deployment for tasks such as medical image analysis, industrial inspection and portrait effects.

OneTrainer trains diffusion models on your own machine for people adapting image or video models to their own datasets. Its graphical interface brings dataset preparation, training and output previews into one application.

SAMJ-IJ brings local AI segmentation into ImageJ and Fiji for researchers annotating microscopy images. It turns point or rectangle prompts into object regions, so users can work with Segment Anything models without writing code or leaving their image analysis environment.

Labkit is a local image segmentation plugin for Fiji, aimed at researchers working with microscopy datasets that can be too large for conventional desktop workflows. It supports manual labeling and automated pixel classification, with a design that lets consumer laptops work with images spanning multiple terabytes.

Segment Anything for Microscopy adapts Meta AI's Segment Anything to biomedical images, with models fine-tuned on microscopy data. It's for researchers who need to label cells or structures such as mitochondria in electron microscopy volumes. The software runs locally on Windows, macOS and Linux and is open source under the MIT license.

Distilabel is a Python framework for engineers building synthetic datasets and model-generated feedback with local LLM backends such as Ollama, llama.cpp and Hugging Face Transformers. It also connects to cloud services, including OpenAI and Anthropic; requests through those integrations go to the remote provider. Where inference runs depends on the backend you choose.

LabelMe is a desktop image annotation app for people preparing computer vision datasets. It combines manual drawing with model assistance for labeling objects and outlining image regions.

QuPath is a free, open source desktop application for researchers analyzing whole slide pathology and microscopy images on their own computers. It combines image viewing and annotation with cell segmentation and interactive machine learning, so researchers can examine tissue and classify what they find within the same application. It runs on Windows, macOS and Linux.

Trainable Weka Segmentation is a local image segmentation plugin and Java library for Fiji. It's for researchers and image analysts who want to teach a classifier which image regions belong to each class, then apply that model to other images or stacks. It's free and open source under GPL-3.0.

Supervision is a Python library for developers building applications around computer vision models. It supplies tools for working with predictions and datasets, so you don't have to build those parts of an application yourself.

PixlStash is an image manager for photographers culling shoots, creators reviewing AI images and people preparing datasets. It helps you find relevant pictures and review which ones to keep in large collections.

Data-Juicer is a Python framework for researchers and teams preparing datasets for AI training, evaluation, agents or document retrieval. It processes data on your own machine or across a Ray cluster.

SLEAP helps behavioral researchers measure animal movement in recorded video. It trains models to estimate poses for single animals or groups, with human review during dataset creation and prediction checking.

Prodigy is an annotation tool for developers and research teams building training and evaluation datasets for custom AI models. It runs on your own machines and provides a web interface where annotators can label data without programming knowledge.

Label Studio is a platform for teams preparing training datasets or reviewing AI outputs. It brings human annotation and evaluation into a browser interface that can run locally or on your own server.

CVAT is a data annotation platform for teams preparing datasets for computer vision and audio models. It combines labeling tools with shared assignments and review workflows.

FiftyOne is a local tool for computer vision teams that need to inspect their training data and understand where their models fail. It runs on macOS, Windows and Linux, with Docker support, and is open source under Apache 2.0. Its Python tools and visual App connect dataset curation with model evaluation.

DeepPoseKit is an open-source Python toolkit for researchers measuring animal movement and behavior in images or video. You define the body points you want to track and train a model on your own annotations. It runs through TensorFlow and Keras on your own hardware, with Windows support documented, and uses the Apache 2.0 license.

Lightning Pose is an open-source package for researchers who estimate animal poses from video for behavior analysis. It supports 2D and 3D pose estimation with single-camera and multiple-camera datasets, including footage where body parts become hidden from view.

DeepEthogram assigns predefined behavior labels to individual video frames. It's designed for neuroscience researchers measuring animal activity, and can also serve other projects that need action detection at frame level.