Image annotation services are becoming increasingly popular as it enables analysis and synthesis of information. Image annotation techniques can be of significant use to businesses across different industry sectors, including e-commerce, and helps online sellers maintain an efficiently searchable product database. However, maintaining the required infrastructure and hiring an in-house team is time-consuming and involves huge operational costs and administrative hassles.
Related article: An overview: What is image annotation?
According to Cognilytica, the market for AI and machine learning data preparation, a process that relies heavily on people to manually label data is expected to more than double, reaching $1.2 billion by 2023. Third-party providers expect to see a significant uptick in that growth, going from $150 million of the market to $1 billion over that same time frame
Outsourcing image annotation services is one of the most crucial tasks for AI companies seeking training to develop the models. Machine learning training data is a kind of fuel that works for algorithms to learn from various patterns and predict in the same way. Let’s understand in detail.
The image annotators are playing a vital role in machine learning with an unlimited supply of images annotated accurately helping machines to recognize the varied objects through computer vision and understand like humans.
Image annotators label the data using the right annotation techniques to create a huge quantity of training data for AI & machine learning. Without annotated images, it is very hard to make objects recognizable for machines.
Image annotators annotate images for machine learning to provide the supervised training data for ML. Each image is labeled or categorized with specific types of annotation for object detection, and these annotated images are used in a supervised machine learning process to create a fully-functional AI model.
Apart from providing the training data, image annotators also play an important role in validating the images produced by the machine learning models. These annotators manually check the machine-backed annotated and correct if any discrepancies are visible there that helps to improve the accuracy of machine learning model prediction.
Apart from this, these annotators also play an important role in machine learning. They can help machine learning engineers to pick the right and most suitable training data for their model development. Being working as a creating the world-class training data for machine learning, image annotators improve the quality and help to build an accurate model.
Image annotation is the only technique that helps machine learning or AI-based perception models recognize the particular object in an image and learn to detect such objects when used in real-life. And there are different types of image annotations, like polygon, polylines, landmark, semantic segmentation, and bounding box which is one the most common image annotation techniques used to make the objects recognizable in 2D and 3D formats.
2D Image annotation includes both labeling of the whole image and labeling all pixels, In such annotations of multiple similar images, can be simplified when the datasets are clustered based on a visual similarity measure, allowing the user to link labels to clusters instead of going individually through all images.
2D and 3D images annotations done as per the project’s requirements, in self-driving model training, 3D annotated images are more helpful and give a more precise perception of an object, while in various sector like retail and automated farming, 2D annotated images are also enough to allow machines detect the objects for future prediction.
The advantages of choosing image annotation services include:
It provides a flexible pricing structure that is affordable. Based on your requirements, we will design a competitive pricing structure without any extra costs.
If data security is preventing you from outsourcing, then you no longer have to worry, because it stringent data security measures in place to safeguard your electronic data.
The quality check ensures that all your images are accurately annotated to help you maintain an easily searchable product database.
Infrastructure and workforce to provide you with correctly annotated images in short delivery periods.
The annotation experts understand the exact context in which these services are used and can also customize annotation services to cater to client requirements.
The imperative need for image annotation in different sectors:
Healthcare professionals rely on visual data to guide diagnosis and treatment. This visual data emerges from modes of imaging such as CT, MRI, x-ray scans, and other types of medical imaging.
Once a patient is scanned, the data must be interpreted by an experienced medical professional. This creates a prime opportunity for computer vision. Image annotation can be used to train CV systems to identify patterns and pinpoint hairline fractures, tumors, abscesses, and much more.
When it comes to healthcare, computer vision has the opportunity to improve the accuracy of diagnosis and treatment, eliminate backlogs, free up expensive scanner slots, and cut down patient wait times.
Similarly, visual search and search connexion square measure the AI-supported method employed in looking out the proper things on eCommerce websites serving to folks to shop for things as per their means with fewer efforts. In retail inventory management categorized times unbroken on the rack are found through AI-enabled machines or robots square measure trained to detect the proper parcels at repositioning with full automation of logistical and provide chain management.
Agriculture remains one of the oldest industries in human history. Precision agriculture, however, is relatively new and involves the practice of combining technology with traditional farming techniques—boosting productivity, profitability, and sustainability.
Examples of precision agriculture include the use of robots, drones, GPS sensors, and autonomous vehicles to expedite farming processes that were once completely manual.
Computer vision systems can be trained to predict crop yields, determine plant health, optimize soil conditions, and much more. Image annotation would be central to these processes, allowing machine learning algorithms to pick up on specific cues, much like experienced farmers would.
Self-driving cars are no longer in the realm of science fiction. However, part of the reason why autonomous vehicles are not as commonplace as they could be is due to safety concerns.
For an autonomous vehicle to be deployed with confidence, the machine learning algorithms powering these vehicles must be extremely robust. This requires an immense amount of training data. That’s where high-quality image annotation comes in.
The aftermath of the COVID-19 pandemic has increased demand for AV technology, including autonomous vehicles and grocery-carrying drones. The future of machine learning—and the transportation industry—is reliant on image annotation.
Here the key points to be considered while outsourcing the image annotation services.
Based on your project needs, it is important to properly identify how your data will be handled. So, here you need to decide before handing over your data, how you want your image annotation to be verified.
Image classification is the best example you can consider while checking the task of the annotators with selecting the appropriate label from each image. The human-powered image classification process can be affected by biased decisions.
Different companies have different standard systems used in data entry and annotation. But the hard-and-fast verification process takes a long time and maybe available at additional costs that can spike your budget and spending on your project.
Evaluating the quality of the services of a company you need to check the historical background and work done by the company in the past. Yes, check the portfolio or data sample produced by the company. And if possible for the demo or similar approaches to making sure it can meet your data annotation needs.
To check that you can go to the website of the company, check the portfolio, clientele to examine the image quality and annotation precision. Most of the companies provide graphics in various formats to represent their workbench. And few of them already have samples with annotated images from the different fields.
Apart from the verification of data, you also need to share the standard of quality you are looking for in each annotated image. Many companies claim to provide accurate training data but what is the meaning of accurate in your terms? You need to clearly define your quality of standard at the time of assigning the project to image annotation companies. And
also, give the example of what kind of quality or standard level you expect in image annotation.
You also need to explain the exact format and type of file with data batched and quality control system you want to implement into your company. For that, you can request a trial project while paying some small charges to check the quality and accuracy level. It will help you to check their speed, quality, and other aspects while performing image annotation on a real-time basis.
Every machine learning training data company has its business model and workflows, and staffing system with specialization in particular annotation types and fields or industry to determine who is exactly going to work on your project.
Companies provide onsite, remote, and both types of image annotation as per the needs and feasibility of the clients. You need to ask these things to companies providing or not, what are the qualifications of annotators and their training level. So, you should ask here these questions about such companies.
Although, for image annotation, certain specialized qualification is not required, except medical images like CT Scan, MRI or X-rays. If you are outsourcing healthcare-related fields for medical imaging analysis, make sure the company has all the required resources and such experts from a medical background to ensure accuracy.
Last but not the least, it is very important to decide the right platform for image annotation. However, annotating with the company’s platform has its benefits like the company owns the platform and can customize the functions as per your project. Another advantage is the specialized annotators are full-aware of the user-interface and functions of the platform and they don’t need additional training to learn how to operate.
While on the other hand, if you require annotated images on your own, you may have to pay the extra fees to train your staff to operate such software.
Third-party service providers have the skills and knowledge about working with the latest technology in this field. They have precise capturing tools for making the images recognizable for machine or computer vision.
Outsourcing image annotation services can help you streamline the entire process. It provides customized services within the fastest turnaround time. Outsourcing can help you get your best return on investment. You can even leverage the time difference by getting work delivered within the next day.