Human-Powered Image Annotation Services for Computer Vision Training Data
In the age of AI, machines are getting trained to learn and perceive the environment just like humans to understand everything when exposed to similar things in real-life scenarios. And for computer vision- based AI models, data with labels or annotations can help the machines learn such things.
There are different things visible in their natural environment and when we use machines to deal with various things, we must be aware of the specifications and other attributes of such things. So, right here we need to understand thoroughly more about Image Annotation, why it is used, and what are the popular techniques and other aspects associated with this process.
What is the meaning of annotating the images ?
Image annotation is the task of annotating the objects of interest in the images with the right labeling techniques and tools. The annotation task can be performed manually, or with the combination of AI- assisted software to make the labeling process faster and more precise.
Image labeling helps to generate a huge amount of training data for machine learning algorithms the accuracy of AI models very much depends on the quality and quantity of datasets used while training the model. This process makes the objects recognizable to machines with the scope of making such objects easily perceivable in a different kind of environment.
How Images are Annotated ?
Let’s take an example given below in which the bounding box annotation technique has been used to capture the objects we need to make recognizable to machines. In this case, pedestrians are annotated in blue and taxis are marked in yellow shades.
And this is repeated into the different images as per the project’s requirement or depending on the business use cases. Though, the quantity of labels in each image varies as per the project requirements. Few projects need the only annotation to represent the content of the whole image that helps in image classification. While on the other hand, few projects could require multiple objects to be labeled within a single image, each with a different label such as bounding box annotation.
Types of Image Annotation
To create innovative and more useful datasets, machine learning engineers and data scientists can choose the different types of annotation types. So, let’s find out to compare and summarize the most common annotation types within computer vision.
Annotation for Object Classification: With image classification, the main motive is to simply identify what objects and other properties exist in an image.
Annotation for Object Detection: To detect the object or find out the position of the individual objects and it is possible with bounding box annotation.
Annotation for Image Segmentation: Finally image segmentation to recognize and understand the images with pixel level. In this annotation, every pixel is an image that belongs to at least one class, as compared to object detection wherein the bounding box annotation objects can overlap.
In the image, classification is a broad categorization of an image and useful for unsupervised learning as it associates with an entire image with just one label. The best part of this it is one of the easiest and quickest ways to annotate the images compare to other techniques. A whole-image classification is a good option for abstract information like scene detection and time of day. Whereas, bounding boxes are used for object detection to offer a balance between quick annotation speeds and targeting items of interest.
“Image annotation in machine learning” is picked to support the AI models where you need to know whether or not an image contains the object of interest and objects that are not for our interest. This is compared to other annotation types like classification or bounding boxes that is faster to annotate but less accurate than the AI models trained through deep learning algorithms.
The Role of Human-powered Image Annotation Services
To annotate the bulk of images, AI companies seek image annotation companies having such annotators to perform the task of annotation. These annotators are trained with the specifications and other guidelines to annotate images for each annotation project, as different companies working on different types of AI visual perception models have different requirements.
And once these annotators are trained for doing this task, they will start annotating thousands of images using the training data platforms providing the data annotation features. And such training data platforms are specially designed software having all the tools to use different types of annotation. This software is equipped with multiple tools allowing annotators to sketch complex shapes.
But here again, using or annotating the images using the tools, the workforce of skilled annotators are required. Human-powered annotation becomes more productive and effective here where the AI- assisted data labeling process is followed. The right combination of human skills and tool efficiency is used to produce better quality training data for AI models that can predict with high accuracy.
Cogito is one of the pioneers in image annotation with an edge over its competitors. Working with a team of well-trained and experienced annotators working with the most advanced tools and techniques for producing the high-grade training datasets for AI models developed for diverse industries.
It is offering the most affordable data labeling solution for AI companies seeking the most precise training datasets for robotics, drones, and self-driving cars. From healthcare to robotics, retail, automotive, and agriculture, Cogito has experience in diverse industry verticals. “Application”
