Discover the 9 most popular image annotation tools that you need to know about heading into 2023. Compare their features and pricing, and choose the best image labelling tool for your use case.

We get it – 

Finding and implementing a high-quality image annotation tool in your computer vision CVOps pipeline can be a hard and tedious process. 

With SO many platforms, tools, and solutions on the market, it can be hard to get a clear understanding of what each tool offer, and which one to choose.

Whether you're a computer vision team looking for the best image annotation platforms for your expert radiologists to accurately label polyps in MRI scans.

Or a data science team working on large-scale defect inspection looking to outsource image labelling – this guide will help you identify the right annotation platform for your needs.

In this post, we will be covering the top image annotation tools for computer vision as of 2023.  We will compare them based on criteria such as data types

Here’s what we’ll cover: 

1. Encord
2. Scale
3. CVAT
4. Labelbox
5. Dataloop
6. Appen
7. Playment
8. V7
9. Hive

Encord Active 

Encord Active is an open source active learning and data curation toolkit focused on helping ML engineers find failure modes in their computer vision models, prioritizing data to label next, and driving smart data curation to improve model performance, reduce annotation costs, and understand your models better.

Encord Active supports model-assisted data debugging in the form of Quality Metrics which makes it good for object detection, segmentation, and classification problems. The software is Open source and runs well on all platforms: Linux, MacOS, and Microsoft OS. However, Encord Active does not support NLP features.

Benefits & Key features:

• Vast library of Quality Metrics to understand your data
• Opportunity to build custom metrics based on image characteristics, metadata, tags, embeddings, etc. to support data curation
• Built-in annotation tool
• Leverages smart similarity search based on machine learning algorithms
• Supports image processing and data augmentation
• Supports model-assisted data and label debugging
• The only data curation tool for healthcare with specialized support for medical imaging

Best for:

Companies looking to power their data curation process. Encord Actrive is not only the preferred solution for mature computer vision companies but also the best for companies just starting out and looking for a free and open source toolkit to add to their MLops or training data pipelines. 

Open source license:

Encord Active is available under an Apache-2.0 license. Read our docs for more on how to self-host Encord Active and see here for the GitHub repo.

Further reading:

• Closing the AI production gap
• The most exciting application of computer vision in 2023

Sama 

Sama Curate employs models that interactively suggest which assets need to be labeled, even on pre-filtered and completely unlabeled artificial intelligence datasets.

This smart analysis and curation optimize your model accuracy while maximizing your ROI. Sama can help you identify the best data from your “big data” database to label so that your data science team can quickly optimize the accuracy of your deep learning model. 

Benefits & Key features:

• Interactive embeddings and analytics
• Machine learning model monitoring
• On-prem deployment
• Provides a streamlined process for corporates

Best for:

The ML engineering team looking for a tool with a workforce. 

Open source license:

Sama does currently not have an open source solution.

Superb AI DataOps 

Superb AI DataOps ensures you always curate, label, and consume the best machine learning datasets. Use SuperbAIs curation tools to curate better datasets and create AI that delivers value for end-users and your business.

Make data quality a near-forgone conclusion DataOps takes the labor, complexity, and guesswork out of data exploration, curation, and quality assurance so you can focus solely on building and deploying the best models. Good for streamlining the process of building training datasets for simple image datatypes.

Benefits & Key features:

• Similarity search
• Interactive embeddings
• Model-assisted data and label debugging
• Good for object detection as it supports bounding boxes, segmentation, and polygons

Best for:

The patient machine learning engineer looking for a new tool.

Open source license:

Superb AI does currently not have an open source solution.

FiftyOne

Originally developed by Voxel51, FiftyOne is an open-source tool to visualize and interpret computer vision datasets.

The tool is made up of three components: the Python library, the web app (GUI), and the Brain. The Library and GUI are open-source whereas the Brain is closed-source.

FiftyOne does not contain any auto-tagging capabilities, and therefore works best with datasets that have previously been annotated. Furthermore, the tool supports image and video data but does not work for multimodal sensor datasets at this time.

FiftyOne lacks interesting visuals and graphs and does not have the best support for Microsoft windows machines.

Benefits & Key features:

• FiftyOne has a large “zoo” of open source datasets and open source models.
• Advanced data analytics with Fiftyone Brain, a separate closed-source Python package.
• Good integrations with popular annotation tools such as CVAT. 

Best for:

Individuals, students, and machine learning researchers with projects not requiring complex collaboration or hosting.

Open source license:

FiftyOne is licensed under Apache-2.0 and is available from their repo here. FiftyOne Brain is a closed source software. 

Lightly.AI 

Lightly is a data curation tool specialized in computer vision. It uses self-supervised learning to find clusters of similar data within a dataset. It is based on smart neural networks that intelligently help you select the best data to label next (also called active learning read more here).  

Benefits & Key features:

• Supports data selection through active learning algorithms and AI models
• On-prem version available
• Interative embeddings based on metadata.
• Open source python library

Best for:

ML Engineers looking for an on-prem deployment.

Open source license:

Lightly.ai’s main tool is closed-source but they have an extensive python library for self-supervised learning licensed under MIT. Find it on Github here.

Scale Nucleus

Created in late 2020 by Scale AI, Nucleus is a data curation tool for the entire machine learning model lifecycle. Although most famously known as a provider of data annotation workforce. The new Nucleus platform allows users to search through their visual data for model failures (false positives) and find similar images for data collection campaigns. As of now, Nucleus supports image data, 3D sensor fusion, and video.

Sadly Nucleus does not support smart data processing or any complex or custom metrics. Nucleus is part of the Scale AI ecosystem of various interconnected tools that streamline the process of building real-world AI models.

Benefits & Key features:

• Integrated data annotation and data analytics
• Similarity search
• Model-assisted label debugging
• Supports bounding boxes, polygons, and image segmentation
• Natural language processing support

Best for:

ML teams & teams looking for a simple data curation tool with access to an annotation workforce.

Open source license:

Scale Nucleus does currently not have an open source solution.

ClarifAI  

Clarifai is a computer vision platform that specializes in labeling, searching, and modeling unstructured data, such as images, videos, and text. As one of the earliest AI startups, they offer a range of features including custom model building, auto-tagging, visual search, and annotations. However, it's more of a modeling platform than a developer tool, and it's best suited for teams who are new to ML use cases. They have wide expertise in robotics and autonomous driving, so if you’re looking for ML consulting services in these areas we would recommend them.

Benefits & Key features:

• Integrated data annotation
• Support for most data types
• Broad model zoo similar to Voxel51
• End-to-end platform/ecosystem
• Supports semantic segmentation, object detection, and polygons. 

Best for:

New ML teams & teams looking for consulting services.

Open source license:

ClarifAI does currently not have an open source solution.

There you have it! Top 7 Data Curation Tools for computer vision in 2023.

Why Is Data Curation Important in Computer Vision?

Data curation is critical in computer vision because it directly affects the performance and accuracy of models. Computer vision models rely on large amounts of data to learn and make predictions, and the quality and relevance of that data determine the model's ability to generalize and adapt to new situations.

Conclusion

In conclusion, data curation is a crucial aspect of any computer vision project. Without good data curation practices, your models may suffer from poor performance, accuracy, and bias. To ensure the best results, it is essential to have the right data curation tools. 

In this article, we have covered the top 7 data curation tools for computer vision of 2023, comparing them based on criteria such as annotation support, features, customization, data privacy, data management, data visualization, integration with the machine learning pipeline, and customer support.

 We hope that this article has provided valuable information and insights to help you make an informed decision on which data curation tool is best for your specific use case and budget. In any case, it is important to keep in mind that tool selection should be based on your specific needs, budget, and team size.

Want to start curating your computer vision data today? You can try an open source toolkit for free:

"I want to get started right away" - You can find Encord Active on GitHub here.

"Can you show me an example first?" - Check out this Colab Notebook.

"I am new, and want a step-by-step guide" - Try out the getting started tutorial.

If you want to support the project you can help us out by giving a Star on GitHub :)

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