Top 6 Tools for Active Learning in Machine Learning
Discover the 6 most popular free, open-source, paid active learning tools to help you kickstart your active learning journey.
Let me ask you a question that will help you determine if active learning is right for you - do you have abundant unlabeled data, but is manual labeling too expensive? The best active learning products help you actively query a user for labels with the highest return-on-investment - a form of 'iterative' supervised learning.
In fact, with the advent and explosion of deep-learning based ML-models, and more recently foundation models, active learning is becoming ever-more important to bridge the gap between prototype and production AI models. Active learning is even heralded as the future of generative AI. The main goal of active learning is to intelligently curate training data and improve model performance by surfacing the most informative data samples for labeling to reduce uncertainty in your computer vision model and reduce the cost of manually labeled data.
Navigating all the different tools and frameworks can be a headache - so to help you, we have compiled a list of the most popular machine learning tools focused on active learning for computer vision on the market.
Whether you are:
- A data scientist looking for a practical way to discover potential edge cases and outliers to identify scenarios where your model might fail -
- A data operations team looking to model data drift over a large dataset to find the most informative subsets of data to label -
- Or a CTO looking to reduce an enormous bill for manual annotation
This guide will help you compare the top active learning tools and help you find the best one for you.
We will compare each based on key features - including interactive visualization functionality, acquisition functions, other active learning algorithms, support for different data and annotation types, project size, integration with annotation and human validation tools, and customer support.
We'll update our review frequently to ensure you can stay on top of notable releases and developments in this exciting and fast-moving space!
The 6 most popular active learning tools:
Encord Active
Encord Active (GitHub repo can be found here) is an open-source active learning toolkit that helps automatically find and fix dataset errors and biases, explain and improve model performance, and intelligently curate your data. It's the best option for teams that:
- Are looking for a solution that integrates tightly with state-of-the-art automated annotation and workflow tools to enable real-time active learning workflows-
- Are already deploying or aiming to deploy discriminative or generative models in production environments soon -
- Are building advanced artificial intelligence applications requiring a diverse set of pre-defined and custom parametrizations (or "Quality Metrics") added onto their training data and models -
Encord Active was designed to compute, store, inspect, manipulate, and utilize quality metrics for various functionality. It hosts a library of these quality metrics and, importantly, allows you to customize by writing your metrics to calculate/compute quality metrics across your dataset.
Benefits & Key Features:
- Open-source and deployed version
- Specialized in computer vision with support for a broad set of visual modalities
- Native integration with standard annotation tools and Encord Annotate, a state-of-the-art AI-assisted labeling and workflow tooling platform -
- Advanced data curation features
- Support for all annotation types - bounding box, polygon, polyline, instance segmentation, keypoints, classification, and more -
- Supports evaluating your training data based on a trained model and imported model predictions with acquisition functions such as entropy, least confidence, margin, and variance with pre-built implementations
- Visual interface with data distribution, image similarity, correlation, and image embeddings exploration functionality
- Allow you to systematically evaluate and rank the quality of your data and labels against pre-defined or custom metrics, such as brightness, image singularity, annotation duplicates, closeness to image borders, occlusions in video or image sequences, frame object density, and many more
- Advanced Python SDK and API access (+ easy export into JSON and COCO formats)
Best for:
- Teams looking for an integrated and secure commercial-grade enterprise platform encompassing both annotation tooling and workflow management alongside an expansive active learning feature set and -
- Data science, machine learning, and data operations teams who are seeking to use pre-defined or add custom metrics for parametrizing their data, labels, and models.
Pricing:
Encord Active is open-source under the Apache-2.0 license and is available as a hosted and fully integrated version of the Encord platform.
Further reading:
- Model Test Cases: A Practical Approach to Evaluating Machine Learning Models
- 4 Ways to Debug Computer Vision Models [Step By Step Explainer]
- Closing the AI Production Gap with Encord Active
- We Employed ChatGPT as an ML Engineer for a Day - This Is What We Learned
Lightly
Lightly is a platform that combines active learning with data curation, annotation, and management, enabling users to create high-quality training datasets with minimal effort. Its AI-powered active learning techniques help users prioritize the most relevant and informative data points to label, leading to improved model performance.
Benefits & Key Features:
- Web interface for data curation and visualization
- Supports image, video, and point cloud data for computer vision tasks
- Supports active learning strategies such as uncertainty sampling, core-set, and representation-based approaches
- Integrations with popular annotation tools and platforms
- Python SDK for seamless integration into existing workflows
Best for:
- Data scientists and machine learning engineers who want an intuitive, end-to-end solution for active learning, data curation, and annotation tasks
Pricing:
Lightly offers a free tier with basic features and limited usage. Paid plans with additional features and scalability start at $280 per month.
Cleanlab
Cleanlab is a popular open-source tool focused on data-centric AI. It provides algorithms and interfaces to help companies across a broad set of industries improve the quality of their datasets and diagnose and fix various issues. Cleanlab offers three main products: Cleanlab Research, Cleanlab Open-source, and Cleanlab Studio.
Benefits & Key Features:
- Open-source through Cleanlab Opens-source and deployed version, Cleanlab Studio
- Supports images, text, and tabular data for classification tasks
- Scoring and tracking features to continuously monitor data quality over time
- Visual playground with a sandbox implementation
Best for:
Individual researchers and smaller teams looking to solve simple classification tasks and find outliers across different data modalities.
Pricing:
Cleanlab Open-source is open-sourced under the GNU General Public License v3.0 and is available as a hosted version in Cleanlab Studio.
FiftyOne
Voxel51 is the company behind FiftyOne, an open-source toolkit designed to enhance computer vision workflows by improving dataset quality and providing valuable insights into deep learning model performance. FiftyOne empowers teams to collaborate securely on datasets in the cloud, streamlining the process of creating, curating, and managing high-quality data for machine learning models.
- Effortlessly explore, search, and slice datasets to find samples and labels that meet specific criteria.
- Leverage tight integrations with public datasets or create custom datasets to train models on relevant, high-quality data.
- Optimize model performance by using FiftyOne to identify, visualize, and correct failure modes.
- Automate the process of finding and correcting label errors to curate higher quality datasets efficiently.
- Utilize the FiftyOne Brain for scalable identification of edge cases, mining new samples for training, and more.
- Build data-centric pipelines with FiftyOne and PyTorch to surface high-quality data and develop production-ready models more efficiently.
Best for:
Data scientists and machine learning engineers working on computer vision projects who seek an efficient and powerful solution for data visualization, curation, and model improvement, with an emphasis on data quality and building streamlined workflows allowing for rapid iteration.
fastdup
fastdup is an open-source tool that helps data scientists and machine learning engineers identify and remove duplicate or near-duplicate images from their datasets. Doing so enables users to create cleaner and more diverse training datasets, ultimately improving model performance.
Benefits & Key Features:
- Helps to identify wrong labels, outliers, and corrupted data
- Offers additional features such as graph search, clustering, and visualization
- Built on a C++ graph engine that can handle a large number of images on a single CPU
Best for:
Data scientists and machine learning engineers who need a fast and efficient way to identify and remove duplicates from their image and video datasets.
Pricing:
fastdup is open-source under the Creative Commons Attribution 4.0 Interlational Public License. The company behind fastdup, Visual Layer, looks to be working on a commercial-hosted enterprise version.
modAL
Modal is an open-source active learning library for Python3 built on top of scikit-learn by the developer Tivadar Danka. It is designed to help users efficiently label their datasets by selecting the most informative instances.
Benefits & Key Features:
- Open-source library with a focus on modularity and extensibility
- Supports a variety of active learning strategies, such as query-by-committee, margin sampling, and uncertainty sampling
- Compatible with popular machine learning frameworks, such as scikit-learn and TensorFlow
- Python-based implementation with extensive documentation and examples
Best for:
Individual researchers building prototype and sandbox machine learning applications looking seeking a scikit-learn based Python3 library for active learning.
Pricing:
modAL is open-source under the MIT 2.0 license.
Conclusion
That's all, folks! The Top 6 Tools for Active Learning in Machine Learning.
Active learning is a crucial component in modern machine learning pipelines, enabling data scientists and machine learning engineers to make the most of their data and improve model performance efficiently. With a wide range of tools available, choosing the one that best fits your specific needs and requirements is essential. This guide explored six of the top active learning tools, including Encord Active, Cleanlab, Lightly, Voxel51, fastdup, and modAL. Each tool offers unique features and benefits that cater to different use cases, data types, and project scales.
To make the right decision, consider factors such as the level of integration with annotation tools, support for various data modalities and annotation types, active learning strategies, and pricing. By selecting the best active learning tool for your needs, you can optimize your labeling efforts, create high-quality training datasets, and ultimately enhance the performance of your machine learning models.
For further reading, you might also want to check out a few other honorable mentions:
- MONAI - if you are working with medical imagery and are looking for tooling not present in Encord Active
- Argilla - if you are working with NLP-based AI applications
- Labelbox Catalog - if you are looking for a data curation tool for organizing, searching, visualizing, and exploring labeled and unlabeled data
Watch for updates and developments in this rapidly evolving field as new tools and features are introduced. As you embark on your active learning journey, we hope this guide has provided valuable insights to help you make the best choice for your specific needs and goals.
Related Blogs
