Data labeling or annotation plays a crucial role in developing accurate machine learning (ML) models. By properly tagging raw data like images, text, audio, video, etc., businesses enable their AI systems to make reliable predictions. However, many organizations have unique data annotation needs that off-the-shelf solutions cannot fully address. This is where open source data labeling platforms come in.
Open source data annotation tools empower companies to customize existing solutions to their specific requirements without having to build everything from scratch. They allow businesses to tweak functionality by integrating new code into data labeling workflows. When budget or time is limited, leveraging open source software is an effective data strategy.
In this comprehensive guide, we will first examine the value of open source data annotation platforms. Next, we will thoroughly explore the top 10 open source data labeling tools available today. For each platform, we will cover its vital capabilities, supported data types, languages/frameworks used, and more.
The Vital Role of Labeled Data in ML Models
Before diving into the platforms, it‘s important to understand why quality labeled data matters in machine learning.
ML models rely on labeled datasets to learn to make accurate predictions. The labels act as guides that enable models to identify patterns and relationships within data. For instance, an image classification model needs many pictures correctly tagged with labels like "dog", "cat", "car" etc. to learn visual features of these classes.
According to Anthropic, a prominent AI safety startup, labeled data is one of the most significant drivers of ML model performance. Their research shows that with 10x more labels, an AI system‘s error rate dropped by 3-4x on language tasks and 6-9x on vision tasks.
This demonstrates the sizable improvements in model accuracy that comprehensive labeling of training data can unlock. However, annotation at scale is also complex and costly. Per Figure Eight, annotation costs can range from $20 per hour for basic tasks to $250+ per hour for specialized domains like medicine.
Additionally, many companies have specific annotation needs that generic data labeling platforms cannot address appropriately. In such cases, open source annotation tools that allow customization provide an optimal solution.
The Promise of Open Source Data Labeling Platforms
Open source data annotation platforms offer compelling advantages that make them a preferred choice for many ML teams and data scientists:
Full Customizability – Since the source code is freely available, developers can modify open source software to add new features or tweak existing functionality. This allows creating solutions tailored to a company‘s unique requirements.
Cost-efficiency – There are no licensing fees associated with open source annotation tools. The initial investment lies in integrating and scaling the platforms, making open source a prudent and budget-friendly option.
Community Support – Active user communities behind open source projects provide useful documentation, updates, tips, and other resources to help implement the platforms.
Interoperability – Open standards used in open source software promote seamless interoperability with other systems a business may be using like data stores, BI tools, etc.
Transparency – The public availability of source code allows thoroughly vetting an open source platform‘s security, privacy standards, and compliance with regulations.
Agile Innovation – Because many developers worldwide collaborate on improving open source software, these projects tend to rapidly innovate and evolve compared to proprietary solutions.
For data annotation needs unmet by off-the-shelf solutions, open source platforms empower businesses to cost-effectively customize workflows without straining time or resources. Next, let‘s explore the top 10 open source data labeling/annotation tools:
Top 10 Open Source Data Labeling/Annotation Platforms
1. Computer Vision Annotation Tool (CVAT)
CVAT is an open source data labeling tool focused on computer vision tasks like image classification, object detection, segmentation, tracking, etc. Originally created by Intel, it supports standard dataset formats like PASCAL VOC, COCO, DATASET, and more.
Key Capabilities:
- Annotation tools for images, video, and 3D point clouds
- Diverse labeling, drawing, tracking, and automatic annotation capabilities
- Built-in collaboration features like review workflows
- On-premise or cloud deployment options
Supported Data Types: Image, Video, 3D Point Clouds
Languages/Frameworks Used: TypeScript, React, Python, CSS
Source Code: GitHub
With out-of-the-box support for major computer vision tasks, CVAT is an excellent choice as a customizable annotation platform.
2. Label Studio
Label Studio is an end-to-end data annotation tool supporting text, image, audio, video, and time-series data. It can handle diverse ML use cases like classification, named entity recognition, object detection, segmentation, and more.
Key Capabilities:
- Domain-specific labeling UIs for different data types
- Native integration with ML frameworks like TensorFlow, PyTorch, XGBoost
- Real-time data validation and quality monitoring
- REST API for customization and integration
Supported Data Types: Text, Image, Audio, Video, Time-series
Languages/Frameworks Used: Python, React
Source Code: GitHub
With robust support for diverse data types and machine learning frameworks, Label Studio is ideal for large-scale data annotation projects.
3. Doccano
Doccano is an open source text annotation platform for teams. It streamlines collaboration for text classification, sequence labeling, sentiment analysis, and other NLP tasks.
Key Capabilities:
- Intuitive interfaces for text tagging, sentiment analysis, etc.
- Robust collaboration tools like user management, access control, etc.
- Database support for PostgreSQL, MySQL, SQLite
- Customizable via Python API and webhooks
Supported Data Types: Text, Sequences
Languages/Frameworks Used: Python, JavaScript
Source Code: GitHub
Doccano simplifies team collaboration on text annotation projects required for many common NLP tasks.
4. ImageTagger
ImageTagger is an open source image annotation tool for computer vision tasks. It supports bounding box, semantic segmentation, and polyline annotations.
Key Capabilities:
- Annotate images via bounding boxes, masks, or polylines
- Detailed image analytics and inspection capabilities
- Keyboard shortcuts for efficient annotation
- Collaboration features like user management and project management
Supported Data Types: Image
Languages/Frameworks Used: Python
Source Code: GitHub
With robust keyboard support and analytics, ImageTagger excels at scaling image annotation for computer vision.
5. OpenLabeling
OpenLabeling is an open source data annotation platform supporting images, videos, text, and audio. It can label data for classification, object detection, segmentation, OCR, and speech recognition tasks.
Key Capabilities:
- Custom labeling interfaces for diverse data types
- Real-time validation and statistics during annotation
- REST API and integration with ML frameworks
- On-premise or cloud hosting options
Supported Data Types: Image, Video, Text, Audio
Languages/Frameworks Used: Python, Vue.js
Source Code: GitHub
The customizability of OpenLabeling makes it great for diverse data annotation projects.
6. VGG Image Annotator (VIA)
The VGG Image Annotator (VIA) is an open source image annotation tool developed at Visual Geometry Group, University of Oxford. It supports region-based annotation for tasks like object detection, segmentation, and image captioning.
Key Capabilities:
- Manual and automatic annotation tools
- Region, rectangle, ellipse, polygon, and point annotation types
- Standalone HTML application with offline support
- Integration with TensorFlow, Keras, PyTorch, etc.
Supported Data Types: Image
Languages/Frameworks Used: JavaScript
Source Code: GitHub
VIA is an excellent open source solution tailored for region-based image annotation tasks.
7. Voice Loop
Voice Loop is an open source audio annotation tool optimized for speech data. It can label audio clips for speech recognition, speaker ID, sentiment analysis, and more.
Key Capabilities:
- Real-time audio waveform visualization
- Tools for segmenting, labeling, and transcribing speech
- Customizable via React library and REST API
- Secure media and data management
Supported Data Types: Audio
Languages/Frameworks Used: JavaScript, Python
Source Code: GitHub
For speech-focused audio annotation, Voice Loop is an excellent open source platform.
8. PixelAnnotationTool
PixelAnnotationTool enables efficient semantic segmentation annotation for images and videos. Users can quickly label objects, regions, textures, etc at a granular pixel level.
Key Capabilities:
- Annotate via polygons, cuboids, ellipses and pixel-level segmentation
- Object tracking across video frames
- Data augmentation capabilities
- Support for medical images in DICOM format
Supported Data Types: Image, Video
Languages/Frameworks Used: Python
Source Code: Github
For semantic segmentation model training, PixelAnnotationTool is a powerful open source solution.
9. ASADI
ASADI is an open source audio annotation tool developed by the Data Science Institute at University of Virginia. It streamlines collaborative audio transcription and labeling.
Key Capabilities:
- Real-time audio waveform visualization and playback
- Segmenting, transcribing, and labeling audio sections
- Project dashboard for managing team collaboration
- Customizable via React UI library and Python API
Supported Data Types: Audio
Languages/Frameworks Used: Python, JavaScript, React
Source Code: Github
ASADI simplifies team audio annotation workflows for speech, music, acoustic events, etc.
10. Comic Annotations
Comic Annotations is an open source tool for annotating images in comic books and manga. It supports speech balloon segmentation, text extraction, and translation.
Key Capabilities:
- Segment and label speech balloons in comic panels
- Extract and translate text from balloons
- Annotate comic book metadata like volume, genre etc.
- Customizable React components
Supported Data Types: Images (comic books, manga)
Languages/Frameworks Used: JavaScript, React
Source Code: GitHub
For annotating comics and manga, Comic Annotations provide an open source platform tailored to this unique use case.
Key Takeaways
The open source data annotation platforms explored above demonstrate the diversity of options available today. From images and video to text, speech, and more, these tools help teams customize labeling workflows to their specific requirements in a budget-friendly manner.
Here are the key takeaways:
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Full Customizability – Open source enables modifying tools to suit unique annotation needs without having to build in-house solutions entirely from scratch.
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Supportive Communities – Active user forums provide helpful resources for integrating open source annotation platforms.
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Specialization – Leading options offer robust tooling tailored for tasks like semantic segmentation, transcription, sequence labeling etc.
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Easy Collaboration – Built-in features help facilitate multi-user distributed annotation projects.
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Interoperability – Support for standard data formats like COCO, CSV, etc simplifies dataset creation.
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Transparency – Public source code allows vetting tools for security, privacy, compliance etc.
Clearly, open source data labeling platforms empower businesses to achieve their diverse annotation goals without breaking the bank.
Contact us to guide you in selecting the most suitable data annotation tools for your machine learning initiatives after assessing your specific requirements and use cases. Our advisors can recommend top annotation service partners to scale your AI applications efficiently.
Additional Resources on Data Labeling
To learn more about data annotation and explore additional popular data labeling tools, refer to these resources: