What are the Limitations of Automatic Image Annotation vs Manual?

Image annotation has become integral part of AI development to create the training data for machine learning. Its helps to make the objects recognizable for machines into images. And as much as annotated images as a data is available for training the machines the accuracy level of prediction would be higher allowing AI developers to make the right model.

In race of supplying such training data companies are using the automatic route to annotate the images by machines and get the high volume of data. As AI-based image annotator tools and software has been developed for such needs and they can produce large amount of annotated images in the less time period fulfilling the needs of machine learning engineers.

Using automatic image annotation tools you can annotate large quantity of images in less time but it not necessary it will solve your computer vision problem as there are many challenges in automatic image annotation problem faced by the users.

Inflexibility of Work due to Variability in Projects

Automatic image annotation can be useful if the images are available as per the automatic image annotation tool of software compatibility. Any kind of new project or variable images need the new software or make the adjustments in setting the annotation technique is one of the major challenge while working with automatic image annotation. While manual or human-powered image annotation solve such problems, as they can make changes in annotation if they get any different types of image they are flexible to adjust accordingly.

Not Suitable for Unsupervised Learning Process

Similarly, automatic image annotation is also not suitable for unsupervised machine learning as machines will find difficult to allocate and classify the objects in images. As in unsupervised learning images are not labeled or annotated, machines needs to classify the them as per their algorithms and programming to make it recognizable in different class. Here, manual image annotation works better, as human can identify the new objects and annotate the same to make it recognizable for machine learning models.

Lack of Scalable Solution for Turnaround Demand

Another significant challenge for automatic annotation is machines are designed or developed to produce certain quantity of products. Similarly, here in automatic image annotation, if large quantity demanded becomes impossible to achieve the desired output. But human-powered image annotation solution can be expanded as per the additional demand by the customers. Human-powered image annotation provides scalable solution for turnaround demands allowing AI companies get the training data as per the customize needs.

Anolytics offers image annotation service for wide-ranging using the most sophisticated form of image annotation techniques for wide range of industries need training data for AI and machine learning. Provides high-quality data annotation services for image, video and texts for autonomous vehicles, agriculture, healthcare and retail etc. It is working with advance level of image annotation tools and software with human-powered annotation solution for all types of annotation needs required as a training data for machine learning and AI models.