When training a new AI pipeline, the annotation process can be painful and quite costly. In most of the projects, it is one of the main bottleneck in the release of impactful AI pipelines. Then, leveraging existing AI pipelines to automatically generate new annotations is a way to accelerate this process.
This repository is aiming to provide a curated list of existing resources demonstrating automatic data labeling/annotation. The information is split by data type (image, text, video, sound, ...) and each of this type is split between:
- list of public repositories
- list of private tools
- list of scientific publications
Warning, the intent here is to provide a list of sources claiming to use/implement auto labeling/annotation. Not all of these sources have been tested/verified and there is no guarantee that the expressed claims are valid.
- autodistill/autodistill: Locally annotate from a foundation model and train a specific model on top of it.
- ultralytics/ultralytics: Auto annotate from Segment Anything Model (SAM) applied to Yolo detections.
- Nvidia Tao Auto Label: run GroundingDINO to annotate images (local or cloud).
- yasho191/SwiftAnnotate: Generate multimodal annotations using main LLMs and VLMs (private and public ones).
- AlvaroCavalcante/auto_annotate: Locally annotate from a pretrained object detection model after setting up a threshold.
- mailcorahul/auto_labeler: CLI tools allowing to leverage different foundation models to generate annotations on local data.
- akuonj/omanAI: Running Yolov8 to add bounding boxes to images.
- fexploit/ComfyUI-AutoLabel: generate text description from images.
- Antraxmin/AutoLabeler: Use Ultralytics to generate annotations on images.
- pollyjuice74/CVAT-Auto-Labeler: Use Ultralytics to generate annotations on images coming from CVAT.
- Daniele-Cannella/auto_labeling: Use a Phi 3 and grounding to make new annotations on images.
- jamesnatulan/auto-image-labeler: Use foundation models to make zero shot annotations in images.
- Ziad-Algrafi/ODLabel: Label new images by applying Yolo-World model.
- sah4jpatel/AutoLabeller: Label new images by applying Yolo-World model.
- aiXander/CLIP_assisted_data_labeling: Few shot learning to add label from clip embeddings.
- ChunmingHe/WS-SAM: Generate full segmentation mask in medical images from weak annotations and SAM.
- StriveZs/ALPS: Leverage SAM to generate annotations on remote sensing images.
- Fudan-ProjectTitan/OpenAnnotate3D: Open-vocabulary auto-labeling system for multi-modal 3D data.
- CVAT Automatic Labeling: General computer vision annotator
- Label Studio Auto Annotation: General computer vision annotator
- Anylabeling: General computer vision annotator
- fabrylab/clickpoints: General computer vision annotator
- halleewong/ScribblePrompt: User interface to auto-annotate medical images from click prompting.
- filonenkoa/autoannotator: User interface with human face automatic annotations
- Babyhamsta/Yoable: User interface with YoloV8 automatic annotations
- FennelFetish/qapyq: User interface allowing to add captions to images using vision language models, and generate bounding boxes from Yolo.
- anujgaut/All-in-One-Label: User interface allowing to run YOLO or Mask R-CNN to generate annotations on images.
- Ashish-Yallapragada/AI_AssistedImageSegmentation: User interface designed to help the annotation of zebrafish images.
- ParthShethSK/LabelPal: user interface allowing to generate annotations from specific tensorflow models (coco-ssd).
- ksugar/qupath-extension-sam: Use SAM to label medical images in QuPath.
- Neural-Symbolic-Image-Labeling/HEPHA: User interface designed to make active learning from already existing image level annotations.
- Roboflow Auto Label
- Encord Data Agent
- Supervisely NN Image Labeling
- Picselia Model Assisted Labeling
- Labellerr LabelGPT
- Labelbox Annotation With Foundation Model
- IBM Maximo Visual Insight
- EpigosAi Auto-Label
- SuperbAi Automated data labeling
- Amazon SageMaker Automated Labeler
- Google Image Labeling
- Unitlab Auto Data Labeling
- Lightly AutoLabeling
- Landing AI Label Assist
- V7 Go
- Sentisight AI assisted image labelling
- CloudFactory Automated Labeling
- Clarifai Automated Data Labeling
- Samsung SDS AutoLabel
- TrainYolo
- Intel Geti
- Digit7 DigitSquare
- Kili Technology Model Assisted Labeling
- Labelme Automated Annotation
- Edge Impulse AI Labeling
- Segments AI Model-assisted labelling
- AI Wakforce AI assisted labeling
- LabelAI AI assisted labeling
- Deepen AI Auto-Label
- DataGym AI-assisted pre-labeling
- JTheta Auto-Label
- Halcon AI-Assisted Labeling
- BasicAI Auto Annotation
- Mots'oehli, Moseli. Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review. 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC). IEEE, 2024.
- Zhuravlyov, A. K., et al. Automatic Annotation of Training Datasets in Computer Vision Using Machine Learning Methods. Automatic Documentation and Mathematical Linguistics 58.Suppl 5 (2024): S279-S282.
- Zhou, Shiyuan, et al. HEPHA: A Mixed-Initiative Image Labeling Tool for Specialized Domains. Proceedings of the 30th International Conference on Intelligent User Interfaces. 2025.
- Adsule, Aniket, et al. SiamALNet: A Semi-supervised Siamese Neural Network with Active Learning Approach for Auto-Labeling. World Conference on Artificial Intelligence: Advances and Applications. Singapore: Springer Nature Singapore, 2024.
- Toubal, Imad Eddine, et al. Modeling collaborator: Enabling subjective vision classification with minimal human effort via llm tool-use. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.
- Vishwakarma, Harit, et al. Pearls from pebbles: Improved confidence functions for auto-labeling. Advances in Neural Information Processing Systems 37 (2024): 15983-16015.
- Zou, Xueyan, et al. Segment everything everywhere all at once. Advances in neural information processing systems 36 (2023): 19769-19782.
- Zhou, Yijie, et al. Openannotate3d: Open-vocabulary auto-labeling system for multi-modal 3d data. 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024.
- Gallagher, James E.,et al. [A Multispectral Automated Transfer Technique (MATT) for machine-driven image labeling utilizing the Segment Anything Model (SAM).] (https://ieeexplore.ieee.org/abstract/document/10815733) IEEE Access (2024).
- Sun, Botao. Autolabeling & Semantic Segmentation with 4D Radar Tensors. Master thesis (2024).
- Cazzaniga, Giorgio, et al. Improving the annotation process in computational pathology: A pilot study with manual and semi-automated approaches on consumer and medical grade devices. Journal of Imaging Informatics in Medicine 38.2 (2025): 1112-1119.
- Diaz-Pinto, Andres, et al. Monai label: A framework for ai-assisted interactive labeling of 3d medical images. Medical Image Analysis 95 (2024): 103207.
- Deshpande, Tanvi, et al. Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation. arXiv preprint arXiv:2404.17033 (2024).
- He, Chunming, et al. Weakly-supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping. Advances in Neural Information Processing Systems 36 (2023): 30726-30737.
- Kim, Doyun, et al. Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model.. Nature communications 13.1 (2022): 1867.
- Liang, Mingfu, et al. Aide: An automatic data engine for object detection in autonomous driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.
- Alamikkotervo, Eerik, et al. TADAP: Trajectory-Aided Drivable area Auto-labeling with Pretrained self-supervised features in winter driving conditions. IEEE Transactions on Intelligent Vehicles (2024).
- Bist, Ramesh Bahadur, et al. Efficient auto-labeling of large-scale poultry datasets (ALPD) using an ensemble model with self-and active-learning approaches. arXiv preprint arXiv:2501.10809 (2025).
- Yang, Chen, et al. Annolid: Annotate, Segment, and Track Anything You Need. arXiv preprint arXiv:2403.18690 (2024).
- Zurowietz, Martin, et al. MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration. PloS one 13.11 (2018): e0207498.
- Zhang, Song, et al. Alps: An auto-labeling and pre-training scheme for remote sensing segmentation with segment anything model. IEEE Transactions on Image Processing (2025).
- Schlonsak, Ruben et al. Bridge Detection in Autonomous Shipping: A YOLOv8 Approach with Autodistill and GroundedSAM. Journal of Physics: Conference Series. Vol. 2867. No. 1. IOP Publishing, 2024.
- Matuzevičius, Dalius. A Retrospective Analysis of Automated Image Labeling for Eyewear Detection Using Zero-Shot Object Detectors. Electronics 13.23 (2024): 4763.
- Fan, Shu-Kai S., et al. Auto-Labeling for Pattern Recognition of Wafer Defect Maps in Semiconductor Manufacturing. Journal of Manufacturing Science and Engineering 146.7 (2024).
This section is listing some code repositories with less than 1 year activity.