Blog #image annotation

Things To Consider While Outsourcing The Image Annotation Services 

Outsourcing image annotation services is one the most crucial task for AI companies seek training to develop the models. Actually, machine learning training data is a kind of fuel works for algorithms to learn from various patterns and predict in the same way.

And you need to be very careful while outsourcing your project to such companies to get the best quality data at least cost. So, in context of the same we brought here the key points to be considered while outsourcing the image annotation services. 

#1 How to Verify the Authenticity of Data?

On the basis of your project needs, it is important to properly identify how your data will be handled. So, here you need to decide before hading 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. Here human-powered image classification process can be affected by biased decisions. 

So, to avoid such preconception check the data processing from one stage to another stage. As one annotator labels the image while double-blind verification, two annotators label the image without sighting each other’s task. And if the labeling is not up to the mark, then it is the responsibility of a supervisor to observe the cases and decide the correct label to ensure the quality. 

And finally, multi-blind verification, at least three or more annotators label the images without viewing each other’s work. And here if labels don’t match each other’s data,  consensus or adjudication  is used. With consensus, majority rules and the most common label among the annotators is chosen. While with adjudication, a supervisor is brought in and decides the correct label.  

Different companies have different standard systems used in data entry and annotation. But hard-and-fast verification process take longer time and may be available at additional costs can spike your budget and spending on your project.    

#2 Check the Demos or Data Samples

Evaluating the services quality 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 make sure it has capability to 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 providing the graphics in various formats to represent their workbench. And few of them already have samples with annotated images from the different fields.  

#3 Specify the Quality of Standard You Required 

Apart from verification of data, you also need to clearly share the standard of quality you are looking in each annotated image. Many companies claims to provide the accurate training data but what is the meaning of accurate in your terms. 

Actually, there are multiple types of image annotation techniques like bounding box, cuboids, polygons and semantic segmentation but what kind of annotation you are looking for your AI or ML project. 

Actually, in some annotations the picture need to zoom at very large size to annotate the objects at very edge to adjust the annotation at pixel levels. Though, few projects may allow the margin of error, while many of them not allow a single margin and need 100% accuracy in each image to get the accurate results.    

So the important points here is, that 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 real-time basis. 

#4 Decide Who Will Annotate Your Images 

Every machine learning training data company has its own 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. 

Actually, 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 to 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 such fields for medical imaging analysis, make sure company have all the required resources and such experts from medical background to ensure the accuracy.  

#5 Decide the Right Platform for Annotations

Last but not the least, it very important to decide the right platform of image annotation. As some companies have their own platform while few of them use other third-party platform to annotate the images and charge additional fess for suing that. 

However, annotating with company’s platform has its own benefit like company owns the platform and can customize the functions as per your project. And another advantage is the annotators are full-aware with 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 have your own custom platform or you are using third party platform that you want to use. If you require annotated images on any other external platform as per your choice, you may have to pay the extra fees to train your staff to operate such software, but make sure inquire about platform fees.    

However many of the companies use the best software to annotate the images with their annotators. And if they are meeting your needs, you should go on with their sources to optimize the cost of your AI project development. 

Anolytics provides the the image annotation outsourcing services using the best tools and annotation software to annotate the each image with precision to render the pixel-level data labeling service. It is expert in providing the wide-ranging data annotation solution with best accuracy while ensuring the pricing. 

Anolytics is specialized in image annotation for various industries like automotive, retail, robotics, agriculture, autonomous flying and healthcare. It is providing the complete image annotation solution using the various techniques like bounding box, semantic segmentation, polygon annotation, landmark annotation, 3D point annotation and 3D Cuboid Annotation for different types of AI and ML projects.  

Ref. link : https://anolytics.home.blog/2019/12/05/how-to-decide-important-points-while-outsourcing-image-annotation/

Annotation and labeling of Images is highly in demand owing to growth in AI and machine learning (ML) developments. The annotation and labeling helps the models developed on AI or ML algorithms to learn from set of data and use for future prediction.    

Use of Annotations and Labeling Images

The AI and ML models are trained with visual perception based technology called computer vision, that can only visualize the objects through images or videos. So, the object of interest in the images are annotated or labeled with special technique. And once such images are labeled at large scale used as a training data.

The annotated images are used to train the algorithms used in AI and ML training. The algorithm helps machines to learn the certain patterns and store that into virtual memory to correlate or utilize the same while analyzing the similar data comes into real-life use.

How To Label Image Dataset?

The annotated images used as a machine learning training data are labeled at large scale by experts using the image annotation tools or software. The huge amount of images from a specific sector is uploaded or stored on the database of the software and then annotators annotated each image with precision. 

There are different types and techniques of image annotation is used to label such images, so that objects become recognizable to machines through computer vision. 

Types of Image Annotations Used 

Bounding Box, Polygon Annotation, 3D Cuboid,  Semantic Segmentation and Landmarking, there are different types of image annotations used to label image datasets, depending on the algorithms and model compatibility.     

Once the images are annotated they are feed into the algorithms to train the model, and to ensure the accuracy, whether model has been trained properly, the ML Model Validation Service is also used by machine learning engineers. 

 As much as amount of labeled with variations is used as training data while developing the AI or ML models, the accuracy would be higher. Though, it very difficult to define how much training data is required to train the ML algorithms.

How To Annotate Images For Deep Learning?

The quality of image annotation service for AI, ML or deep learning is very important to ensure the accuracy of model prediction. So, images should be well-annotated by the experts so, that machines can easily and accurately recognize the objects. 

And use of right tools or image annotation software is also very important to make sure annotator can precisely annotate the each image to produce the quality training data making an AI and ML model successfully developed. 

And for deep learning you should have the best quality training data that can train your model precisely as per the expectations of machine learning engineers. So, its better to get the images annotated by the industry professionals.  

Anolytics is one of the well-known companies, providing the image annotation and data labeling service for machine learning and AI model developments. It is providing the image annotation solution wide range of industries like healthcare, retail, robotics, agriculture, self-driving cars and autonomous flying etc as per the needs.

Ref. link : https://anolytics.home.blog/2020/01/03/what-is-the-use-of-image-annotations-how-to-label-image-dataset/