Image annotation outsourcing

Introduction to image annotation outsourcing:

When and in what instances is it necessary to mark up images, and how can Toloka help? If you’re looking to learn more about image annotation outsourcing, this article can serve as a helpful starting point.

Using Toloka as our baseline, we dive in by defining image annotation services, highlighting examples and use cases, outlining the requirements, techniques, challenges, and benefits, and explain how to choose the right platform when it comes to image annotation outsourcing services.

What is image annotation?

Image annotation involves detecting and labeling objects in images to train AI and machine learning models. Human labelers perform a key role in this process by labelling images or tagging pertinent information using an image annotation tool. The structured data that is collected as a result is then fed to a machine learning model (known as “training a model”).

Computer vision models can be taught to recognize objects in images — the results of which depend on the quality and accuracy of the training data. Computer vision allows machines to “see” and interpret the world around them, much like people can.

Examples and use cases for image annotation

There are many scenarios in which image annotation can be useful, including:

E-commerce

• E-commerce cataloguing

• Product search relevance

• Recommendation systems

Computer vision tasks

• Object detection and object recognition

• Optical character recognition (OCR)

• Image classification

Online content

• Image comparison

• Image tagging

• Image moderation

Let’s take a closer look at some of the points listed above starting with image annotation for object detection. Using a platform like Toloka, you can leverage the collective power of the crowd to annotate particular objects and whole images to train computer vision models for various purposes from facial recognition to self-driving cars and product identification.

When it comes to image classification, for example, the crowd matches pictures to categories by labelling images based on pre-selected class names.

With side-by-side comparison, Toloka compares two pictures or photos and leverages the collective feedback of the crowd to determine which one is better.

Text recognition is another example where image annotation comes in handy. Manual labeling can be used to identify and transcribe text in images.

You can also collect raw data by creating a customized image dataset by asking the crowd to submit photos that meet your company’s requirements.

Semantic segmentation is another area where image annotation can be highly useful. A class is assigned to every pixel in an image to determine the target object's exact edges and boundaries.

Furthermore, in Toloka you can label data and feed them to your pre-trained models, to fine-tune them or validate their results.

Image annotation requirements and image annotation techniques

In addition to semantic segmentation, side-by-side comparison, and image classification as discussed above, there are several other image annotation techniques, including the use of:

Lines and splines

To delineate traffic lanes, routes, sidewalks, electrical lines, and other boundaries for various uses, such as autonomous vehicles, drones, storing robot paths, and more.

2D bounding boxes

A 2D bounding box is used to count and track objects, for example, in surveillance videos and aerial images. Annotators draw a rectangle around the entire image of an object they intend to annotate.

Cuboid or 3D bounding boxes

3D bounding boxes deliver detailed insights about an object's length, depth, and width.

Polygonal image segmentation

Annotators use polygon techniques to accurately annotate irregular forms. Polygons help with true shape representation.

Skeletal or key points

To identify and annotate an object’s motions. For example facial features in facial recognition, such as characteristics, gestures, expressions, poses, and more.

Be sure to use a fully equipped image annotation tool to annotate images according to your project’s requirements. Consider selecting a reputable platform like Toloka that provides high quality image and annotation services.

Image annotation challenges

There are several challenges that exist when it comes to image annotation. These include:

Errors

Manual annotation is subject to human error, irrespective of the annotators’ level of expertise. Adding control tasks can help reduce the number of errors.

Time and costs

Labeling each data element and assembling extensive datasets can be a costly, labor intensive, and time-consuming process. Crowdsourcing,AI-assisted automated labeling, and automation serve as viable solutions.

Skills gap

If a company doesn’t have the necessary talent pool, one solution is to divide complex tasks into microtasks. Experts provide detailed instructions and control the results, while crowd contributors do the labeling.

Inconsistency

When cross-labeling datasets, multiple annotators may have conflicting views on certain labels. Data scientists employ different aggregation techniques to ensure accuracy and consistency. Toloka provides a full range of aggregation tools, from classic categorical aggregation to advanced algorithms for every possible scenario, including pairwise comparisons and non-categorical text and image segmentation. See our Crowd-Kit Python library for a full list.

Benefits of image annotation process outsourcing

Image annotation outsourcing can help companies fill knowledge gaps by outsourcing tasks to data and annotation services vendors comprising experts who collect and label raw data on the company’s behalf from start to finish. As experienced professionals, they know which methods work best for certain data types, how to annotate mass data, clean structured and semi-structure data, organize new sources of different dataset types, and more.

Some of the key advantages include:

Time saving

A reputable data and image annotation services vendor provides you with the talent needed to streamline, scale, and rapidly adjust your image annotation tasks.

Scalability

Outsourcing can help you deal with changes in volume, talent gaps, resource constraints, and development of AI models, among other factors.

High data quality

You can leverage the expertise, tools, and techniques of data annotation companies to fit your projects.

Speed and efficiency

Dedicated data annotation specialists have the necessary skills and domain knowledge to produce high-quality training data quickly and easily.

Remove bias

To prevent internal bias in training data, you can outsource data annotation work to diverse and experienced annotators and reduce inaccurate and unfair outcomes. While there are many pros to outsourcing, including deep data expertise, technical infrastructure, and quality assurance, there’s also the obvious drawback of potentially incurring significant expenses for these services.

Crowdsourcing, as a subset of outsourcing, can be a great alternative. This data annotation collection method is becoming increasingly popular due to its significant time- and cost-saving benefits. Crowdsourcing is generally a more financially viable option that doesn’t require specific technical training or pricy software purchases. As a major plus, tasks can be carried out in just a matter of hours or even minutes.

Rather than having fewer narrowly trained specialists tackle various data-related tasks, with crowdsourcing, you can draw on the collective effort of thousands of crowd contributors to acquire accurate, high-quality data. You can also gain access to contributors with specific profiles and skillsets (such as rare languages, mechanical training, and more) that often don’t exist elsewhere.

Choosing the right platform to provide image annotation services

There are multiple factors and considerations to take into account when deciding which platform to select for your image annotation tasks. Firstly, data protection, security, and privacy should be top of mind. Secondly, the following steps should be taken to ensure tasks are carried out successfully:

1. Task decomposition

Larger tasks should be broken down into more manageable pieces as separate project tasks.

2. Clear instructions

Detailed instructions with clear examples should be provided to data annotators for more accurate results.

3. Intuitive interface

The platform’s interface should be user friendly and allow data annotators to submit labeling tasks easily and quickly.

4. Quality control

Quality control tools (like CAPTCHA, speed monitoring, and action checking) should be integrated into labeling projects to ensure high-quality results.

5. Flexible pricing

The best price reflects a compromise between fair compensation for crowd contributors and a requester’s budget.

6. Verification of results

Once submissions are made by data annotators, the results should be aggregated, and tests should be run to ensure quality and accuracy.

How Toloka can help with image annotation services

Toloka provides a safe and efficient way to label your images using the collective power of the crowd. From the platform’s architecture and design to protecting user rights and anonymity, data security is a top priority. In addition to GDPR compliance, Toloka's information systems are certified according to ISO/IEC 27001 and ISO/IEC 27700.

Toloka uses a secure software development lifecycle process that includes, among others:

• Manual security testing (white box penetration testing)

• Regular codebases scanning

• Continuous DAST scanning

• Automatic security checks on third-party components

• Security analyses of images used in cloud platforms

Additionally, image labeling in Toloka is easy. All you need to do is create a project, customize your settings, collect data, get results, and make any small tweaks to improve outcomes for your next batch of data. Our experts can help you at every step of this process, feel free to reach out!

Our platform is designed to meet the most challenging data demands offering clients fast scalability, short turnaround time, a wide range of quality control tools, real-time data labeling, an API and Python SDK for easy integrations, and transparent pricing.

Key takeaways

Outsourcing image annotation can be a preferable option for multiple reasons — to fill a knowledge gap, increase raw data accuracy, draw on the collective contributions of the crowd, and save money, time, and effort in the process.

If you’re looking to outsource image annotation, be sure to:

1. Identify your project goals

Taking into account the project requirements, the type of data you’re working with, your budget, and measures of success.

2. Choose the right provider

Considering whether they have the relevant experience, the best tools and techniques, the security credentials to handle sensitive data, and so on.

3. Monitor for quality

Appraise vendors on their ability to deliver high-quality data along with speed, labeling accuracy, and the number of annotators available.