# Data Labeling and Annotation Tools

> Search results for `label and annotate data for training datasets` on awesome-repositories.com. 104 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/label-and-annotate-data-for-training-datasets

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## Results

- [humansignal/label-studio](https://awesome-repositories.com/repository/humansignal-label-studio.md) (27,619 ⭐) — Label Studio is a multi-modal data annotation platform designed to create and manage high-quality training datasets for machine learning. It functions as a self-hosted, containerized environment that supports secure, private deployments, including air-gapped configurations. The platform provides a centralized workspace for labeling diverse media types, such as images, text, audio, and time-series data, to support supervised and reinforcement learning workflows.

The platform distinguishes itself through deep integration with machine learning backends, enabling active learning loops, automated
- [heartexlabs/label-studio](https://awesome-repositories.com/repository/heartexlabs-label-studio.md) (27,626 ⭐) — Label Studio is a multi-type data labeling tool and data annotation workspace designed to prepare datasets for machine learning training. It functions as a cloud-integrated data pipeline that imports raw data from storage, manages the annotation process, and exports labels into standardized formats.

The platform features a machine learning model integration framework that connects to external model servers. This enables model-assisted annotation and active learning, allowing the system to perform pre-labeling and refine predictions based on human feedback.

The software provides project manag
- [googlecloudplatform/training-data-analyst](https://awesome-repositories.com/repository/googlecloudplatform-training-data-analyst.md) (8,566 ⭐) — This project is a cloud data analysis sandbox and a collection of courseware designed for learning data analysis techniques on Google Cloud Platform. It serves as a training lab containing technical demonstrations and practical exercises for skill development and cloud certification.

The repository provides guided labs and demonstrations focused on Google Cloud data analysis, encompassing technical training for the platform's specific data services. It enables the practice of cloud data engineering and the use of big data tooling to perform queries and data transformations.

The environment s
- [cvat-ai/cvat](https://awesome-repositories.com/repository/cvat-ai-cvat.md) (15,317 ⭐) — CVAT is an open-source, web-based platform designed for annotating images, videos, and 3D point clouds to create high-quality training datasets for machine learning. It functions as a containerized server that orchestrates the entire lifecycle of computer vision data, from initial task creation and manual labeling to quality assurance and final dataset export.

The platform distinguishes itself through deep integration with machine learning models, allowing users to deploy custom AI models as serverless functions for automated object detection, tracking, and skeleton annotation. It supports co
- [conardli/easy-dataset](https://awesome-repositories.com/repository/conardli-easy-dataset.md) (13,394 ⭐) — Easy-dataset is a comprehensive platform designed for the end-to-end management of machine learning datasets, specifically tailored for language and vision model fine-tuning. It functions as a centralized environment for the entire data lifecycle, encompassing the automated generation of synthetic training data, the structural organization of document collections, and the systematic annotation of individual data points.

The platform distinguishes itself through its integrated evaluation and orchestration capabilities. It provides a dedicated suite for benchmarking models, featuring blind side
- [alibaba/mnn](https://awesome-repositories.com/repository/alibaba-mnn.md) (14,242 ⭐) — MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices.

The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse
- [harvardnlp/annotated-transformer](https://awesome-repositories.com/repository/harvardnlp-annotated-transformer.md) (7,325 ⭐) — The Annotated Transformer is an educational resource that provides annotated code implementations of the Transformer architecture for sequence-to-sequence tasks, built with PyTorch. It serves as a learning tool for understanding attention mechanisms, multi-head parallel attention, and scaled dot-product attention through executable examples that walk through each component of the model.

The project covers the full Transformer pipeline, including stacked encoder-decoder layers with residual connections and layer normalization, sinusoidal positional encoding for order-aware representation, and
- [deepfakes/faceswap](https://awesome-repositories.com/repository/deepfakes-faceswap.md) (55,289 ⭐) — Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users to map facial identities between source and destination datasets while maintaining structural alignment and lighting consistency across video frames.

The project distinguishes itself through a highly extensible plugin-based architecture that handles hardware-accelerated process
- [d2l-ai/d2l-en](https://awesome-repositories.com/repository/d2l-ai-d2l-en.md) (29,001 ⭐) — This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation.

The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
- [doctrine/annotations](https://awesome-repositories.com/repository/doctrine-annotations.md) (6,738 ⭐) — This project is a PHP docblock annotation parser and reflection metadata tool designed to extract structured metadata from doc-comments and convert them into class instances. It functions as a system for retrieving and managing custom metadata attached to classes, methods, and properties.

The library includes a metadata caching system to store parsed results, which reduces the performance overhead associated with repeated reflection calls and string parsing. It also serves as a static analysis utility for validating source code structure and enforcing coding standards through automated docblo
- [huggingface/datasets](https://awesome-repositories.com/repository/huggingface-datasets.md) (21,643 ⭐) — Datasets is a library designed for the management, processing, and sharing of large-scale data collections for machine learning workflows. It functions as both a data processing framework and a versioning platform, providing tools to organize, filter, and transform massive datasets while ensuring reproducibility across research and development teams.

The library distinguishes itself by enabling the handling of datasets that exceed available system memory. It utilizes memory-mapped file access, disk-based caching, and lazy iterative streaming to maintain performance when working with large-sca
- [doccano/doccano](https://awesome-repositories.com/repository/doccano-doccano.md) (10,674 ⭐) — Doccano is a collaborative data labeling platform and machine learning dataset management system. It provides a web-based interface for teams to import raw text, mark datasets, and export structured annotations for model training.

The project specifically supports text annotation for classification and named entity recognition tasks. It enables teams to coordinate multiple users on a single project to maintain consistent labeling guidelines and increase the speed of dataset creation.

The system includes tools for data management and team coordination, providing the ability to import raw data
- [vmware/data-annotator-for-machine-learning](https://awesome-repositories.com/repository/vmware-data-annotator-for-machine-learning.md) (61 ⭐) — Data Annotator for Machine Learning
- [therobotstudio/so-arm100](https://awesome-repositories.com/repository/therobotstudio-so-arm100.md) (5,494 ⭐) — SO-ARM100 is an open-source robot arm hardware project providing 3D-printable designs and assembly guides for building affordable robotic arms. It includes calibration software to synchronize motor communication parameters and arm positions via USB, alongside hardware designs for tactile sensing robotic grippers.

The project distinguishes itself through the integration of touch-sensing and flexible filaments for adaptive grasping. It also provides a dedicated imitation learning dataset tool, featuring a web interface for labeling and visualizing robotics data to train machine learning models
- [arumaekawa/dataset-distillation-with-attention-labels](https://awesome-repositories.com/repository/arumaekawa-dataset-distillation-with-attention-labels.md) (23 ⭐) — Implementation of "Dataset Distillation with Attention Labels for fine-tuning BERT" (accepted by ACL2023 main (short))
- [planbrothers/ml-annotate](https://awesome-repositories.com/repository/planbrothers-ml-annotate.md) (109 ⭐) — Use ML-Annotate to label data for machine learning purposes
- [dokploy/dokploy](https://awesome-repositories.com/repository/dokploy-dokploy.md) (34,901 ⭐) — Dokploy is a self-hosted platform-as-a-service designed to simplify the deployment and management of containerized applications and databases. It provides a centralized control plane that decouples administrative management from application workloads, allowing users to oversee infrastructure across multiple server nodes through a unified web interface or a command-line tool.

The platform distinguishes itself through an extensive library of pre-configured application templates, enabling the rapid deployment of databases, identity providers, and various productivity or development tools. It sup
- [gokumohandas/made-with-ml](https://awesome-repositories.com/repository/gokumohandas-made-with-ml.md) (48,343 ⭐) — Made-With-ML is an automated documentation generator and developer experience platform designed to transform source code into structured, searchable reference websites. It functions as a codebase intelligence tool that parses implementation details to provide clear explanations of logic and data requirements.

The system distinguishes itself by leveraging language-level type annotations and structured code comments to generate interface specifications. By utilizing static analysis to extract metadata, it automates the transformation of docstrings into web-ready documentation, ensuring that tec
- [olafenwamoses/imageai](https://awesome-repositories.com/repository/olafenwamoses-imageai.md) (8,867 ⭐) — ImageAI is a Python computer vision library providing a suite of tools for image classification, object detection, and video analytics. It functions as an integrated framework for locating and labeling objects in static images and video streams, utilizing deep learning models for identification and categorization.

The project includes a model training toolkit that allows for the creation of custom classifiers and detectors through scratch training or transfer learning. It features a GPU-accelerated inference engine to increase processing speed for vision tasks and includes specialized utiliti
- [crowdcurio/audio-annotator](https://awesome-repositories.com/repository/crowdcurio-audio-annotator.md) (466 ⭐) — A JavaScript interface for annotating and labeling audio files.
- [jakevdp/pythondatasciencehandbook](https://awesome-repositories.com/repository/jakevdp-pythondatasciencehandbook.md) (48,561 ⭐) — This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping.

The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st
- [open-mmlab/mmdetection](https://awesome-repositories.com/repository/open-mmlab-mmdetection.md) (32,756 ⭐) — This project is a modular research toolkit designed for developing, training, and evaluating deep learning models for object detection, segmentation, and video instance tracking. It provides a flexible training engine that manages complex neural network execution, including distributed training, custom lifecycle hooks, and weight optimization. The framework is built around a hierarchical configuration system that allows users to define architectures, data pipelines, and training hyperparameters through composable, inheritable files.

The project distinguishes itself through its highly modular
- [shizhediao/post-training-data-flywheel](https://awesome-repositories.com/repository/shizhediao-post-training-data-flywheel.md) (65 ⭐) — We aim to provide the best references to search, select, and synthesize high-quality and large-quantity data for post-training your LLMs.
- [apify/crawlee](https://awesome-repositories.com/repository/apify-crawlee.md) (24,002 ⭐) — Crawlee is a web scraping framework designed for building scalable, reliable, and distributed data extraction pipelines. It provides a unified interface for managing headless browser automation and lightweight HTTP requests, allowing developers to handle complex web navigation, dynamic content rendering, and large-scale data collection within a single, modular architecture.

The project distinguishes itself through its resource-aware concurrency controller, which dynamically scales task execution based on real-time CPU and memory usage to prevent host machine exhaustion. It also features a rob
- [qqwweee/keras-yolo3](https://awesome-repositories.com/repository/qqwweee-keras-yolo3.md) (7,116 ⭐) — This project is an object detection framework implementing the YOLOv3 architecture using Keras and TensorFlow. It functions as a deep learning vision model and computer vision toolset designed to locate and classify multiple entities within images and video streams using bounding boxes.

The system includes a multi-GPU inference engine to distribute computational loads across several graphics processing units. It also provides a pipeline for creating custom object detectors by retraining pre-trained weights on annotated datasets to recognize user-defined object classes.

The framework covers m
- [damo-nlp-sg/llm-data-annotator](https://awesome-repositories.com/repository/damo-nlp-sg-llm-data-annotator.md) (6 ⭐) — The repo is the source code for Is GPT-3 a Good Data Annotator?
- [tecoholic/ner-annotator](https://awesome-repositories.com/repository/tecoholic-ner-annotator.md) (595 ⭐) — NER Annotator for SpaCy allows you to create training data for creating a custom NER Model with custom tags.
- [lllyasviel/controlnet](https://awesome-repositories.com/repository/lllyasviel-controlnet.md) (33,942 ⭐) — ControlNet is a framework for structural image generation that extends pre-trained diffusion models with neural network architectures designed for precise spatial control. By injecting structural guidance directly into the latent-space denoising process, the system enables users to enforce geometric or semantic constraints on generated outputs while maintaining style consistency.

The framework distinguishes itself through a weight-locked copying mechanism that preserves the integrity of the original model while introducing new control signals. It supports multi-condition synthesis, allowing f
- [abhineet123/deep-learning-for-tracking-and-detection](https://awesome-repositories.com/repository/abhineet123-deep-learning-for-tracking-and-detection.md) (2,508 ⭐) — This project is a curated research repository and structured index focused on deep learning techniques for object detection and tracking. It serves as a centralized archive for academic papers, datasets, and software implementations, providing a cohesive resource for studying methodologies used in image and video analysis.

The repository distinguishes itself through a systematic approach to knowledge management, utilizing hierarchical file organization and metadata-driven tagging to categorize technical literature. By indexing domain-specific datasets and cross-referencing academic resources,
- [lllyasviel/controlnet-v1-1-nightly](https://awesome-repositories.com/repository/lllyasviel-controlnet-v1-1-nightly.md) (5,156 ⭐) — This project is a neural network extension for Stable Diffusion that provides spatial control and geometric consistency for text-to-image generation. It functions as an image structure controller and conditioning tool, enabling the use of external inputs to guide the layout and geometry of generated imagery.

The framework is distinguished by its ability to transform input images into structural guides through various preprocessors. These include the extraction of depth maps, normal maps, and human pose landmarks, as well as the detection of Canny edges, anime lineart, and straight architectur
- [javserjod/label-app](https://awesome-repositories.com/repository/javserjod-label-app.md) (2 ⭐) — Label App is a free, simple application designed to assist in manually editing, visualizing and labelling your moderate-sized datasets.
- [haifengl/smile](https://awesome-repositories.com/repository/haifengl-smile.md) (6,387 ⭐) — Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models.

The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin
- [yuanxiaosc/bert-for-sequence-labeling-and-text-classification](https://awesome-repositories.com/repository/yuanxiaosc-bert-for-sequence-labeling-and-text-classification.md) (469 ⭐) — This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction.
- [lmnr-ai/lmnr](https://awesome-repositories.com/repository/lmnr-ai-lmnr.md) (2,608 ⭐) — Lmnr is an LLM observability platform and evaluation framework designed for tracing, logging, and monitoring language model executions. It provides the tools necessary to debug agent behavior, analyze performance, and identify failure patterns in AI agents.

The platform differentiates itself through a trace-to-dataset pipeline that converts production logs into labeled test sets for regression testing. It includes a prompt-variant replay engine to compare different prompts or models side-by-side and a state-cached debugging system to replay agent loops without restarting the process.

The sys
- [francescopace/espectre](https://awesome-repositories.com/repository/francescopace-espectre.md) (6,472 ⭐) — Espectre is an edge machine learning framework and motion detection platform that uses Wi-Fi Channel State Information to identify human presence and movement. It functions as a sensing toolkit for ESP32 microcontrollers, enabling the detection of motion through walls without the use of cameras or wearables.

The project distinguishes itself by executing compact neural network classifiers and mathematical detection algorithms directly on the microcontroller. It utilizes a MicroPython runtime to allow for the prototyping and deployment of sensing logic and wireless signal processing algorithms
- [jiachengcheng96/learning-with-bounded-instance-and-label-dependent-label-noise](https://awesome-repositories.com/repository/jiachengcheng96-learning-with-bounded-instance-and-label-dependent-label-noise.md) (5 ⭐) — This is a MATLAB demonstration of the Algorithm 1 in the paper Learning with Bounded Instance and Label-dependent Label Noise . The main program is eval_algo1.m.
- [axolotl-ai-cloud/axolotl](https://awesome-repositories.com/repository/axolotl-ai-cloud-axolotl.md) (12,059 ⭐) — Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large language models. It functions as a comprehensive orchestrator for distributed training, enabling users to manage complex workflows across multi-node and multi-GPU environments. By utilizing structured configuration files, the platform streamlines the setup of training parameters, dataset paths, and hardware distribution strategies.

The project distinguishes itself through its support for diverse training methodologies, including full-parameter tuning, parameter-efficient adaptation,
- [ku21fan/str-fewer-labels](https://awesome-repositories.com/repository/ku21fan-str-fewer-labels.md) (184 ⭐) — Official PyTorch implementation of STR-Fewer-Labels | paper | training and evaluation data | pretrained model |
- [ieriii/spacy-annotator](https://awesome-repositories.com/repository/ieriii-spacy-annotator.md) (125 ⭐) — SpaCy annotator for Named Entity Recognition (NER) using ipywidgets. The annotator allows users to quickly assign (custom) labels to one or more entities in the text, including noisy-prelabelling!
- [crystal-lang/crystal](https://awesome-repositories.com/repository/crystal-lang-crystal.md) (20,299 ⭐) — Crystal is a statically typed, compiled programming language designed for high performance and memory safety. It leverages an LLVM-based compiler to translate source code into optimized machine-executable binaries, while its type-inference-based static analysis enforces strict safety rules during the build process.

The language distinguishes itself through a fiber-based concurrent runtime that manages lightweight execution units for asynchronous input and output without blocking the main process. It also features a powerful compile-time macro system that allows for the inspection and transfor
- [fastai/course22](https://awesome-repositories.com/repository/fastai-course22.md) (3,398 ⭐) — This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks.

The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen
- [cltk/annotations](https://awesome-repositories.com/repository/cltk-annotations.md) (13 ⭐) — A tool for annotating texts using Draft.js
- [facebookresearch/detectron2](https://awesome-repositories.com/repository/facebookresearch-detectron2.md) (34,548 ⭐) — Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration.

The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati
- [chrieke/awesome-satellite-imagery-datasets](https://awesome-repositories.com/repository/chrieke-awesome-satellite-imagery-datasets.md) (3,898 ⭐) — 🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
- [eugeneyan/applied-ml](https://awesome-repositories.com/repository/eugeneyan-applied-ml.md) (29,783 ⭐) — This project is a comprehensive, curated knowledge base designed to support the development and maintenance of production-grade machine learning systems. It serves as a centralized repository of industry-standard technical literature, engineering case studies, and research papers, providing a structured reference for practitioners navigating the complexities of modern data science and machine learning engineering.

The resource distinguishes itself through a cross-domain approach that bridges the gap between academic research and practical implementation. By synthesizing proven industry archit
- [data-creative/next-train-api](https://awesome-repositories.com/repository/data-creative-next-train-api.md) (0 ⭐) — The Next Train API provides a JSON web service for any GTFS feed. Deploy this source code to your own Heroku server to set up an API for your own agency's feed. Let me know how it goes. I'm happy to support you!
- [camel-ai/camel](https://awesome-repositories.com/repository/camel-ai-camel.md) (17,253 ⭐) — This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer.

The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva
- [snorkel-team/snorkel](https://awesome-repositories.com/repository/snorkel-team-snorkel.md) (5,981 ⭐) — Snorkel is a weak supervision system that enables users to programmatically generate training labels for machine learning models without manual annotation. At its core, it provides a framework for writing labeling functions as Python callables that each vote on data points, and then trains a probabilistic graphical model over these multiple weak supervision sources to estimate latent true labels without any ground truth data.

The system automatically learns accuracy and correlation parameters between labeling functions by analyzing observed agreement patterns on unlabeled data, converting lab
- [cymchad/baserecyclerviewadapterhelper](https://awesome-repositories.com/repository/cymchad-baserecyclerviewadapterhelper.md) (24,607 ⭐) — This project is an Android RecyclerView adapter wrapper designed to reduce boilerplate code when building complex lists. It serves as a framework for simplifying data binding and managing the interaction between data models and their corresponding view holders.

The library distinguishes itself through specialized support for multi-type layout rendering, where diverse data models are mapped to specific layouts within a single list. It provides a structural implementation for expandable list frameworks that allow users to collapse or expand hierarchical items to reveal nested content.

Addition
- [valcu/annotator](https://awesome-repositories.com/repository/valcu-annotator.md) (0 ⭐) — annotator provides functions to create image annotations through polygon outlining. Annotator has the same function as graphics::locator() but achieves its purpose through drawing, rather than multiple mouse clicks.