Self-Hosted Data Labeling Tools
Label text, images, audio or video for training and evaluation with self-hosted tools like Label Studio and CVAT.
A proprietary local AI annotation tool for training and evaluation data. Works offline, integrates spaCy and Hugging Face, and supports custom Python workflows.
prodi.gyData Labeling and Annotation
Web#Hugging Face integration#Works offline
Prodigy is a proprietary annotation tool that runs on your own machines, including air-gapped systems without an internet connection. It's for developers and research teams building training and evaluation datasets for custom AI models. The Python library includes a web application where annotators can label data without programming knowledge.
Data Labeling and Annotation+0
A local image captioning model with open weights, Apache 2.0 code, SFW and NSFW coverage, and support for ComfyUI and vLLM.
1.3KUpdated 7 months agoApache-2.0
Windows · Docker#Hugging Face integration#Multimodal input#OpenAI-compatible API
JoyCaption is an open-weight image captioning model for people preparing datasets to train or fine-tune diffusion models. It runs on your own GPU and covers both SFW and NSFW images, including photography, anime, digital art and furry artwork. Automated captions reduce the need to write descriptions by hand or find images that already have usable text.
Data Labeling and AnnotationPhoto Search and Auto-TaggingVision-Language Models+0
An open-source AI agent observability platform with Docker self-hosting, Apache 2.0 licensing, automated failure detection, and regression evals.
3.3KUpdated 2 weeks agoApache-2.0
Docker · Web#LLM tracing#MCP
Laminar is an open-source platform for developers who need to see why an AI agent failed and check whether a fix worked. You can self-host it with Docker or on Kubernetes, including AWS and GCP, or use its managed cloud service. It uses the Apache 2.0 license.
Data Labeling and AnnotationLLM Evaluation and TestingLLM Observability and TracingMCP Servers and Agent Skills+0
A desktop image dataset editor with local AI captioning on CPU or NVIDIA GPU. Runs on Windows, Linux and macOS under GPL-3.0.
1.4KUpdated 12 months agoGPL-3.0
macOS · Windows · Linux#Batch processing#Multimodal input
TagGUI is a desktop app for people preparing image datasets for generative AI training. It combines local AI captioning with manual tag editing, so you can generate descriptions and correct them in the same workspace. It's open source under GPL-3.0 and runs on Windows, Linux and macOS, though macOS doesn't have a packaged release.
Data Labeling and AnnotationPhoto Search and Auto-Tagging+0
An open-source LLM training toolkit that turns documents into specialist datasets, with offline generation on macOS and Linux and optional cloud compute.
1.9KUpdated 3 months agoMIT
macOS · Windows · Linux#Distributed execution#llama.cpp backend#Quantization
Augmentoolkit turns your documents into training data for a custom LLM that learns a particular subject. It's for researchers, developers and hobbyists who want models trained on their own material, such as research papers or fictional lore. The Python toolkit is open source under the MIT license and runs on macOS and Linux, with WSL recommended for Windows.
Data Labeling and AnnotationDatasets and Synthetic DataInference Servers and ClustersLLM Fine-Tuning+0
A desktop image annotation tool for macOS, Windows and Linux, with SAM-assisted masks, text-based labeling and VOC and COCO dataset exports.
16.2KUpdated 18 hours agoGPL-3.0
macOS · Windows · Linux#Multimodal input
LabelMe is a desktop image annotation app for people preparing computer vision datasets. It combines manual drawing with AI assistance for outlining objects and creating labels from text. It runs on 64-bit macOS, Windows and Linux..
Data Labeling and Annotation+0
A Python framework for generating and evaluating LLM datasets, with Apache 2.0 licensing and integrations for Anthropic, Cohere and Argilla.
3.4KUpdated 10 months agoApache-2.0
#Structured output
Distilabel is an open-source Python framework for engineers building datasets to train or evaluate AI models. It pairs synthetic data generation with LLM feedback, so a pipeline can create examples and judge their quality. It uses the Apache 2.0 license.
Data Labeling and AnnotationDatasets and Synthetic DataLLM Evaluation and TestingLLM Libraries and SDKs+0
Open-source AI data processing framework for local machines and Ray clusters, with multimodal cleaning, deduplication and Apache 2.0 licensing.
7.1KUpdated 2 days agoApache-2.0
Docker#Batch processing#Distributed execution#Multimodal input
Data-Juicer is a Python framework for preparing AI datasets on your own machine or a distributed Ray cluster. It's for researchers and teams curating model training data, agent interaction records or documents for retrieval. The project is open source under Apache 2.0.
Data Labeling and AnnotationDatasets and Synthetic DataDocument Parsing for LLMs+0
Open-source Python toolkit for document layout detection and OCR workflows, with pretrained deep learning models and an Apache 2.0 license.
5.8KUpdated 4 years agoApache-2.0
LayoutParser is an open-source Python library for developers and researchers who need to detect page structure in document images and turn OCR output into structured data. Its pretrained deep learning models share a common interface, so you can work with models trained on different document datasets without rewriting the surrounding pipeline.
Data Labeling and AnnotationDocument Parsing for LLMsOCR and Document Scanning+0
A data annotation platform for images, video and 3D point clouds, with an MIT-licensed self-hosted edition, automatic labeling and dataset exports.
16.8KUpdated 1 day agoMIT
Docker · Web#Hugging Face integration#Multi-user access#ONNX
CVAT is a browser-based data annotation platform for teams building computer vision datasets. Its open-source Community edition runs on your own infrastructure with Docker and uses the MIT license. CVAT Online is hosted by CVAT, while the Enterprise offering runs in an organization's own cloud or internal environment.
Data Labeling and Annotation+0
Self-hosted text annotation tool for classification, entity labeling, and summarization datasets. Runs in a browser with Docker support and an MIT license.
10.8KUpdated 8 months agoMIT
Windows · Docker · Web#Multi-user access#Multilingual
Doccano is a self-hosted text annotation tool for machine learning practitioners who need labeled training or evaluation data. It runs on your own machine or server, with a browser interface and Docker support. The software is open source under the MIT license.
Data Labeling and Annotation+0
Open source data labeling and AI evaluation platform that runs locally or on your server, with custom annotation interfaces and model-assisted labeling.
28.4KUpdated 1 day agoApache-2.0
macOS · Windows · Docker · Web#Human approval#Multi-user access#Multimodal input
Label Studio is a self-hosted platform for teams preparing training data or evaluating AI outputs through human review. It handles text, images, audio, video and time series in the same application, including tasks that combine several data types. The open source edition uses the Apache 2.0 license and runs locally or on your own server, with Docker deployment and browser access. A separate hosted cloud edition runs on the provider's infrastructure.
Data Labeling and AnnotationLLM Evaluation and Testing+0
Local diffusion model training software for Windows, macOS and Linux, with full fine-tuning, LoRA, dataset preparation and an AGPL-3.0 license.
3.2KUpdated 1 month agoAGPL-3.0
macOS · Windows · Linux#Inpainting#LoRA
OneTrainer is an open-source application for training diffusion models on your own machine, with dataset preparation and model previews in the same interface. It's for people adapting image or video models with their own training data. It runs on Windows, macOS and Linux under the AGPL-3.0 license.
Data Labeling and AnnotationLoRA and Image Model Training+0
Open-source Python computer vision library for annotating images, tracking objects and managing datasets. MIT licensed, with RF-DETR and Ultralytics support.
51.1KUpdated 1 day agoMIT
Supervision is an MIT-licensed Python library from Roboflow for developers building computer vision applications around their own models. It handles the work around predictions: drawing results on images and video, following objects across frames, and turning detections into counts. It can work with images and datasets on your machine.
Data Labeling and Annotation+0
An open-source AI data annotation tool for human feedback, fine-tuning and evaluation. Run your own server or deploy on Hugging Face Spaces.
5.1KUpdated 1 year agoApache-2.0
Web#Multi-user access#Semantic search
Argilla is an open-source data annotation and feedback tool for AI engineers and domain experts who build training and evaluation datasets. You can run your own Argilla server or deploy it on Hugging Face Spaces. It's licensed under Apache 2.0.
Data Labeling and AnnotationDatasets and Synthetic DataLLM Evaluation and Testing+0
An open-source segmentation model for images and videos. Run it on your own GPU machine, track objects across frames, and refine masks with prompts.
19.9KUpdated 2 years agoApache-2.0
Web#Hugging Face integration
Segment Anything 2 (SAM 2) is Meta's open-source model for selecting objects in images and tracking them through video. It's for developers and researchers who need object masks for visual applications or dataset annotation. The model and its web demo can run on your own GPU machine; Meta also provides a hosted demo.
Computer Vision ModelsData Labeling and Annotation+0
Local AI image manager for photo culling and dataset curation. Run it as a desktop app or self-hosted server, with on-device models and ComfyUI integration.
89Updated 22 hours agoGPL-3.0
macOS · Windows · Linux · Docker · Web#Batch processing#Hugging Face integration#ONNX
PixlStash is an open-source image manager for photographers, AI creators and people curating image datasets. It combines library search with tools for reviewing tags, ranking images and sending selected work through ComfyUI. You can use a desktop app or a self-hosted server with a browser interface.
AI Photo LibrariesData Labeling and AnnotationPhoto Search and Auto-Tagging+0