Why should you outsource your data annotation projects?

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Why should you outsource your data annotation projects?


Data with high-quality annotations are essential while training an image classifier. Even the highest quality of computer vision algorithms is useless if trained on poor quality data. Outsourcing the data annotation projects can ensure the highest quality of datasets. Data annotators are highly trained, skillful, and professional people who have the knowledge to create high-quality annotations. With the help of tools that are mainly meant for data annotation tasks, they develop annotations with accuracy. They have experience and thorough knowledge to deal with edge cases efficiently. Usually, the in-house staff of a company cannot handle such issues. 

Here are the five main reasons why you should outsource the annotation of your image data:


  1. Quality:

The success of image annotation projects depends on the quality of datasets. Image annotators are more professional and skilled than first-time in-house annotators. They know how to create higher quality annotation projects with accuracy using various tools. They do annotation with an image classifier’s perspective. Image annotation services play a crucial role in the success of annotation projects.

For example, the boxes in bounding boxes should neither be too loose nor too tight. Abounding box is considered too tight if it cuts away the parts of the object and too loose if it captures unnecessary parts of the image surrounding. When an object perfectly fits within the box, it’s called a high-quality annotation. Trained annotators ensure the success of annotation projects by using different kinds of annotation tools; they also know when to use semantic segmentation, and when bounding boxes.


  1. Scale:

Large-scale datasets comprising thousands or millions of images are required to create a quality image classifier. Each image in such datasets is annotated. The in-house staff takes a lot of time in the task as they lack the number of employees needed to annotate large datasets. Whereas outsourcing image annotation projects saves time and effort. An image annotation process includes various challenges. In-house image annotators in a company usually get unable to adjust to new demands quickly in a lack of experience and necessary resources. While professional annotators can promptly adapt to changes that occur in the project. 


  1. Speed:

In-house annotators need the training to learn the ways to annotate images. It means the company has to bear a lot of money for the process. Without getting professional training, they may lack accuracy for speed, or they may end up annotating a data set of poor quality. Poor quality data set needs to be annotated more than once. Hence it increases the time of the annotation task.

Your decision to outsource the annotation projects can accomplish the task in a shorter time and can save weeks or even months. Professional image annotators can produce high-quality annotations at high speed. They quickly adapt to new project requirements and increase production speed. 


  1. Data Security:

Data security is of the utmost importance for computer vision projects. Often, companies feel hesitant to take professional image annotation services due to security issues. They think that outsourcing annotation projects may compromise the security of their data.  They are unaware of the fact that image annotation services providers are very well aware of data breaches; they have multiple data security measures for the safety of data. Usually, image annotation companies go with data confidentiality agreements with their clients. They handle their clients’ data with the utmost care and security.


  1. Reducing Internal Bias:

Internal bias, sample bias, and prejudicial bias are the most common types of bias in machine learning. Faulty assumptions about the nature of a dataset end up with machine learning bias. It causes the entire system to suffer. Outsourcing the annotation of images mitigates these biases.

In-house image annotations can lead to internal bias as it expects a model to behave in a certain way, and ignore other possibilities. If training data reflects stereotypes in society, it causes prejudicial bias. If data used to train machine learning does not represent the conditions encountered by the model, it causes sample bias.

Outsourcing data annotation is a cost-effective, specialized, and more focused result-oriented decision.

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