Introduction
Today, the world around you is changing fast. Everything is drastically different to what it was just a few years back. Artificial Intelligence, which is vital in the fast-paced environment where organizations strive to succeed, is growing rapidly. Data Annotation Services is an essential part of Artificial Intelligence.
Data annotation refers to the process of categorizing, labeling and labeling data in order to make it easier for AI applications. Simply put, annotators identify the format they're looking at and then label it. An image, video, audio or text can all be used as the format.
Image Annotation
Annotators are used to label objects in images. This image is an example of a classroom. Annotators label it as: table#1, table#2, chair#1, board, lamp...
There are six types:
- Bounding Box Annotation: Annotators highlight specific object in a square or 2-dimensional shape.
- Cuboid annotation: The annotations label the specified object using a 3-dimensional square shape also known as a cubicle. This annotation can be used for calculating the distance and depth of objects.
- Marker Annotation: The annotators add a small mark around the specified image. This is often used to identify faces, such face recognition is used to unlock a phone.
- Polygon Annotation: This annotation is very similar to the Bounding Box. However, it is more precise as the annotators are able to select the objects they wish rather than drawing squares all over them. This type of annotation can be useful when dealing with aerial imagery. Polygon annotation allows annotators to label roads, streets signs, buildings and trees using Polygon annotation.
- Semantic Section: This method allows for the separation of objects within an image by grouping them into different colored pixels. Annotators create three segments for an image of a road to make this annotation. The first segment includes the people (pixelated blue), while the second section contains the cars (pixelated red) and the third is street signs (pixelated yellow). But, "Instance Segmentation" is a variant of semantic segmentation. Instance Segmentation can create a segment within a segment. That is the only major difference between the two segmentation methods. Annotators are able to differentiate people pixelated blue by creating an inner section and naming them "person#1, person#2, person#3, and person#4". Naturally, person#1 will have a different pixelated colour than person#2 and vice versa.
- Lines & Splines. Annotation: The purpose of this type, is to determine the boundaries and lanes.
Video Annotation
Annotators are able to stop the video and label what is seen. It's the same as Image Annotation but with motion. The types of video annotation include Bounding box, Cuboid Annotation and Polygon Annotation.
Image and Video Annotation form part of the AI field that works only on digital images and video called Computer Vision.
Text Annotation
Annotators label sentences with metadata about selected words. Metadata refers to data about data, in other words, information about the data used. It is similar to highlighting certain words in an academic book. The required sentences are highlighted and then you write on their characteristics. However, instead of writing on them the annotators label them.
There are four types text annotations:
- Sentiment annotation: annotators identify the text according the emotions they feel from it. You can have positive, negative or neutral feelings.
- Intent Label: Annotators indicate the action they want in the text, such as request, command or conformation.
- Semantic annotation: Annotators identify the text using entities as a reference. For example: name, place, date, etc.
- Linguistic Notation: Or Phrase Chunking. Annotators identify the text using grammatical entities such as nouns or adjectives.
Audio Annotation:
Audio clips with different sounds must be recorded before being categorized and labeled. An example is capturing raw data from a party. By using Data Annotation can divide sounds into groups such as: A sentence by person 1, a sentence by person 2, music, or noise. This type of annotation can be used for sound recognition or to create a conversation between a human and a technological device such as Siri. Future refers to Artificial Intelligence. It is important to understand the fundamental processes that will allow your AI and Machine Learning projects to scale.
A primer on Data Labeling Approaches to Building Real-World Machine Learning Applications
Data labeling is a crucial part of machine learning and computer vision operations. Data labeling, for example, is the process of identifying raw images, videos, and audio files, and then annotating them individually to machine learning models. This allows machines to make predictions which can be applied to real-world situations. If a dataset is not properly labeled for self driving cars, it can be used to help the model differentiate between pedestrians or stop signs. But if it's wrongly labeled, it can have devastating consequences. Datasets should contain high levels of detail. Files must also be correctly labeled to ensure that machine learning models get the best results. Machine learning models can be built using either automated or manual methods. It is increasingly important to have accurate computer vision models as artificial intelligence is used more frequently in various scenarios, such as food delivery services, surgeries and warehouse robots.
Human involvement, also known human within the loop, is an important consideration when deciding the right approach for building your machine-learning models. Machine learning models, as they stand now, are not able to work autonomously. They require human oversight in order to create an accurate model. The need for HITL in the current landscape is less than in years passed, but it all depends on the intended approach and goal of a project.
Manual data labeling
Manual data labeling may be the most tedious approach. Manual labeling, which requires high levels of human intervention in order to create a dataset, is the most cumbersome. To build a machine learning training dataset, this method requires humans to manually annotate each image or video. Although manual data labeling is costly and time-consuming it can have certain benefits for certain types of projects.
Manual labeling by trained professionals is the best option for images with lots of data, inconsistencies, and caveats. Hand-labeling by trained medical professionals is the best way to label a computer vision model used to diagnose cancer patients. This rule of thumb is not applicable to a unique automation method or custom labeling AI. However, we will discuss it near the end. The following are some options when training ML model.
1- In-house operations
If a team wants to build its models entirely on-site, it can rely upon everyone. Data scientists and engineers, ML engineers, and interns are all available to help label the thousands of images necessary to create a useful training dataset. If an expert opinion is needed, using their own teams can be advantageous. Tesla, a tech giant, will often have their own internal teams to develop their datasets.
2- Crowdsourcing
Crowdsourcing is where companies hire freelancers to help with data labeling through programs like Amazon Mechanical Turk. Labeling can be done on a small scale with a group of labelers. This reduces both the individual and company-wide workload. This is a great option for outfits with limited resources.
3- Outsourcing
Outsourcing data labeling can be a good option for those who are looking for another route. Outsourcing data labeling is a method where outside workers are hired to label data manually. They are often trained by QA experts and can dedicate their whole attention to labeling.
Automatic Data Labeling
Apart from manual data labels, automatic labeling can also be used for various types of projects. It is often a viable option for many companies. Although there are many variations in automated labeling methods, the most common is an AI system that labels raw data for you. Or AI being integrated within the annotation UI to speed-up manual processes (like converting a boundary box to a segmentation). In both cases, data is reviewed by trained professionals to ensure accuracy and quality. Correctly labeled data is fed into the system to create a data pipeline. While data labeling is not possible to automate, the human touch is sometimes required for complex projects and to validate an AI's performance. However, there are tools and strategies that can greatly streamline and speed the process.
1- Model-assisted labels
Model-assisted Labeling involves labeling an initial Dataset For Machine Learning and then training an AI system parallelly to label the data. The AI system then uses this information for annotations of unlabeled data. You can also use a pre-existing model to predict your data. After the data has been labeled, a human must review it and fix any errors. The model then receives the corrected labels. Some solutions allow this to be done within the UI. Others only allow for the uploading pre-labeled information.
2- AI-assisted Labeling
AI-assisted data labeling is another option that many companies opt to implement. AI-assisted annotation software can be used to help the labeler do manual tasks faster, such as drawing an outline from a small number of points, or making predictions based off previous experience.
3-Auto-Labeling and Custom-Auto-Labeling
The most tedious part of data labeling in artificial intelligence is the hardest. So many companies are seeking ways to reduce the bottleneck through automation. Superb AI wants to challenge the belief that labeling must be tedious and cumbersome. Superb AI, with its no-code platform offers ground truth with just a few hundred labeled pictures or video frames.
Annotation For Machine Learning With GTS
Global Technology Solutions (GTS) provides comprehensive computer vision solutions by giving Annotation Service along with, OCR Datasets and Audio Transcription Services to diverse industries including security and surveillance industrial, transportation smart cities, pharmaceuticals, and consumer electronics through the entire lifecycle of a model, including algorithm selection, learning and validation, through inferencing, deployment and maintenance.
