There are billions of images shared every day on the internet, with each capturing a potentially useful part of the world around us. Images are a rich source of information, and we can use them to train machines to understand our world. However, unlabeled or unstructured data is of very little use in this learning process. Thus, these images need to be labeled or annotated. But what’s the best image annotation tool to do so?
Although image labeling can be done by very simple tools that allow the user to draw over or manipulate the image, as workflows get more and more complex and scale to gigantic levels, using an adequate tool that caters to this complexity becomes increasingly important.
The Comparison Criteria
There are many premium tools on the market that cater to the need of image labeling, providing platforms with features such as ontology / schema management, labeling interface, project management, image import and annotation export support etc.
However, there are various important features that set one platform apart from the other. In this article we will be focusing on the following general features (each having sub-features) that distinguish the different image labeling platforms available on the market. We use these to determine which is the best image annotation tool for you:
How well does the platform cater to non-conventional data types? Nearly all platforms support simple JPGs and PNGs, however support for multi page images, documents (pdf) and medical data that comes in convoluted formats definitely sets one competitor apart from the other.
How well does the platform incorporate various AI assistance features, to make the process of data annotation faster and more efficient? AI Assistance features can include things such as segmenting all objects in an image automatically (Autodetect), segmenting any possible object based on bounding box input (Frame Cut), extracting all readable text from an image (OCR) and following the contour of any possible complex object closely (Smart Scissors). You can see these tools in action here.
How well does the tool encapsulate all possible methods of labeling an object within an image? Nearly all platforms contain tools such as bounding boxes and polygons, however certain use-cases require more specialized tools such as segmentation tool (for instance and panoptic segmentation) and rotated bounding box, for example. This metric compares the competitors on the availability of such tools.
How well does the platform support programmatic functions such as import, export, task assignment through robust APIs and Python SDKs? Since many machine learning teams incorporate the tool in their current ML/AI pipelines, this feature is crucial.
How well does the platform tackle the various aspects of the data labeling process itself? Things such as measuring annotation quality by benchmarks and consensus, complying to secure data protocols, and providing a fully managed service where-in the platform providers (the data labeling company) takes ownership of data to be labeled and delivers the final dataset with quality guarantees.
Feature Comparison Table
We’ve prepared a table to allow for an easy comparison between these tools so that you can factually compare various tools on their comprehensiveness in tackling image labeling. Hopefully by the end you’ll have an idea on what is the best image annotation tool for your own needs.
|Features||Ango AI||Labelbox||V7 Labs||Supervisely||SuperAnnotate||Scale AI||Hasty AI||Redbrick AI|
|Medical (DICOM) Support||✓||✓||✓||✓|
|Document (PDF) Support||✓||✓||✓|
|Multi-page Image Support||✓||✓||✓||✓|
|AI Assistance Tools|
|Rotated Bounding Box||✓|
|Python SDK Support||✓||✓||✓||✓||✓||✓||✓|
|Fully Managed (In-house Annotators Available)||✓||✓||✓||✓||✓|
|Quality Metric Guarantees||✓||✓||✓|
While Ango Hub does much more than image labeling, (comprising text, video and audio labeling), it is easy to observe that even in image labeling it outperforms the others by a wide margin, owing to strengths in all the general features mentioned above. We might be biased, but we think Ango Hub has the potential to be the best image annotation tool for you. Here’s why the platform stands out:
- An incredible range of supported data types allowing teams to tackle any image labeling task.
- More AI assistance tools than any other competitor on the market. Allowing high quality labeling in a fraction of the time, fitting to nearly all use cases.
- Comprehensive developer tools support, with a robust API and python package ready to assist ML teams in their needs.
- In house annotation allowing the user to simply provide the data and receive the finished dataset, with the complete process handled by the company. This makes for a comprehensive operational support allowing for the process of labeling to proceed flawlessly with quality, time and security guarantees.
- A Strong support for the medical domain, allowing for medical teams to provide data in any possible format. The platform automatically anonymizes and allows labelers to efficiently label this data.
Pricing: Free for projects up to 10k annotations, along with Cloud and On-Prem pricing. Feel free to contact sales for more information on pricing.
The platform was launched in 2018 and is a strong player in the image annotation industry, it extends its support for datatypes beyond images, however within images lacks support for certain types. It allows a good suite of image annotation tools with polygons, bounding boxes, brush and nested classifications for annotations.
There is however a complete lack of in-built AI assistance tools allowing for various other competitors to perform better than Labelbox in this domain.
There is ample support for developer tools via an adequate API and python SDK, along with operational support allowing the user to check quality via benchmark and consensus and have the data fully labeled by the labelbox team. Some other interesting features include:
- Human in the loop approach using ML teams’ own models
- Anomaly detection tool to determine outliers.
- Superpixel tool for segmentation.
Price: 5000 images can be labeled for free, along with availability of Pro and Enterprise plans.
A popular platform for image and video labeling, just like Labelbox, the company was founded in 2018 and has since focused heavily specifically on image labeling. While the platform is only meant to tackle image labeling, it allows for certain ML training and deployment capabilities also built in. It has a strong AI assistance tool and Data management / exploring capabilities. The platform does not provide a fully managed labeling service or quality guarantees on the dataset, however with support for various data formats and AI assistance capabilities makes up for them. Key features include:
- A robust class agnostic smart tool similar to Frame Cut
- Simple and effective interface specialized for image annotation
- Adequate medical image annotation support.
- SOC2 and HIPAA compliance
Price: 14-day trial along with credit-based plans. Credits can be used for model training and use of AI assistance features.
Supervisely is a platform for computer vision tasks, for image and videos. Its focus is data annotation however there are various other features of the platform that a data science team may be interested in. A key feature of the platform is that it allows for a high level of extendability through the support of plugins and applications. The fundamental features that make Supervisely stand out are:
- Strong plugin support allowing for extendability
- AI assistance via a tool similar to Frame Cut, along with a tool for smart video segmentation.
- Well documented and supported API and SDK support.
Price: 100 images can be labeled for free, along with availability of Business and Enterprise plans.
A very simplified annotating platform allowing for a great variety of functionality specifically for images, handling complete model generation and training along with an adequate model zoo give this platform an edge. However the annotation experience is itself very basic since no extensive assistance is provided.
Divides the annotation process into various tasks i.e. vector annotations – including boxes, polygons, lines, ellipses, key points, and cuboids – and pixel annotations which allow for segmentation using a brush tool. Important features of this platform include:
- AI assisted labeling using ML teams’ own models
- Model Training and Generation
- Complex Data Querying language incorporated for various functions.
- Intuitive division into vector and pixel annotation
Pricing: Limited number of free images, along with custom Pro and Enterprise plans
One of the leading platforms for data annotation and dataset preparation. A list of impactful clients use this platform. It adopts a generalized approach (tackling nearly all data types (Encompassing sensor, image, video, text and document data). Has very high operational support as the platform is fully managed. It incorporates a powerful data explorer and supports various formats. However apart from auto detect it provides nearly no AI assistance features. The strengths of this platform are:
- Effective data exploration
- Support for a wide variety of data types and image formats
- ML based quality control is also used, combining expertise of models and labelers.
- Scale Collect allows data procurement while Scale Synthetic allows to generate and augment dataset.
Pricing: 2 cents per image, and 6 cents per annotation
A Germany based annotation platform similar in nature to V7 labs, tackling only image annotation, with a strong focus on AI assistance. The platform uses the idea of “using AI to train AI” and incorporates various tools to accomplish this. Key features of this platform include:
- Error Detection via ML powered assistant
- Iterative training over time with better predictions for new images
- Model Development and deployment (by procuring it from Hasty AI)
Pricing: Credit Based, unlimited images. Credits can be used for ML model training and error detection. 30 free credits are provided which can be purchased based on user requirements. 1 Credit = 1 Euro
A well rounded image / video labeling platform with a focus towards medical data annotation. Supports common image and video formats along with Dicom and Nifti for medical data. The platform does not have inbuilt AI assistance tools, however it does incorporate the element of AI assistance through active learning, which is similar in nature to Hasty AI. Key features of this platform are:
- Strong medical annotation support, spherical annotation and 3D reconstruction.
- Active learning incorporation, allowing for fast gains in internal model performance and reduction in labeling times.
- Graphical workflow management system, with multiple stages
- Fully managed workforce with quality guarantees
Pricing: 219$ / month for the first 10k images. After that it costs 1 cent per image
So what’s the best image annotation tool in 2022?
Well, with the plethora of great tools on the market for image labeling it is definitely a tough choice to choose the best image annotation tool for your needs.
If you’re looking to label your image dataset in the best way possible, with a tool that outperforms the competitors in catering to all your labeling needs in terms of efficiency, quality, and the sheer level of cross domain support, don’t hesitate to reach out to us at Ango AI and we’d be delighted to show you how we can assist and partner with you in the journey to produce an incredible dataset.