Artificial Intelligence (AI) and Machine Learning (ML) are fast-growing technologies that help create amazing things like self-driving cars and facial recognition on phones. For these systems to work well, they need a lot of training data. This data can be bought or prepared by experts called data annotators. Data annotation is a tricky and costly job but is essential for making AI systems function properly. In this blog, we’ll explain what data annotation is, the different methods used, and why it’s so important today.
What is data annotation?
Data annotation is when we label data found in videos, images, or text. By labeling this data, models can better understand it and identify specific formats, objects, information, or patterns in the future.
For a model to understand images and videos, it uses computer vision, a part of AI that helps computers see and interpret visual data. This process relies on data annotation, where we label data so the model can learn to recognize objects and patterns. Using the right methods and tools, we precisely label the data to train the computer vision model.
The procedure consists of two steps:
Labelling of data
In this step of data annotation is to tag pictures, videos, or text with labels. This means adding descriptive tags to raw data (like images, text, or videos) to give it context, helping a machine learning model learn from it. For example, in the image below, traffic lights, pedestrians, vehicles, and buildings are tagged to teach the model what they are.
Quality checks and audits
The second step begins once the data is labeled. In this step, the annotated dataset is checked for accuracy and correctness. This is crucial because if the model is trained with incorrect data, it could lead to expensive retraining processes.
Types of data annotation
Image annotation
Image annotation helps understand pictures for various tasks like computer vision, facial recognition, and AI solutions. Before using it, AI models need training with lots of labeled images. This involves adding tags like names and descriptions to hundreds of images. Good training boosts the accuracy of AI models, making them useful for things like self-driving cars and medical diagnosis.
Different types of annotation methods, such as:
- Bounding boxes: Draw a rectangle around the thing you want to label in a picture. Make sure the box touches the very edges of the object for accuracy.
- 3D cuboids: Like bounding boxes, but you also consider depth. Good for labeling things like planes or cars in images.
- Polygons: Instead of a rectangle, draw a shape around the exact thing you want to label. Useful for when other stuff might accidentally get included in the label area.
- Keypoint tool: Annotate an object by marking a series of points. Often used for things like tracking motion or detecting gestures.
Video annotation
Video annotation involves marking objects within each frame to help machines recognize them, similar to how we label pictures with boxes around objects. It’s crucial for teaching computers to find and follow objects in videos, which is handy for tasks like tracking movements. This helps in fields like medicine, manufacturing, and traffic control. Lastly, text annotation involves tagging and organizing written information, which is vital for many businesses that rely heavily on text data.
Text annotation
Text annotation refers to adding metadata or labels to pieces of text.
Adding metadata
Adding metadata is like giving important details to the learning algorithm. This helps it to pay more attention to specific words.
Example: The information added in the brackets helps the learning algorithm understand what it’s been trained for, so it can recognize similar information later on.
Assigning labels
When labels are added, words in a sentence can be categorized to show what kind of sentence it is. For instance, it can show if a sentence is expressing feelings or if it’s technical.
Example: when someone says, “I don’t like this product, I want my money back,” we can tag their feeling as “unhappy.” This tagging helps computers understand feelings and what people want, kind of like when they recognize names in a sentence.
Automated data annotation vs Manual data annotation
Manual data annotation
Manual data annotation, as the name suggests, involves humans:
- Annotators receive raw datasets (videos, images, text, etc.) for labeling.
- They decide which method (bounding box, keypoint tool, etc.) to use based on the project’s requirements.
- Annotators label all necessary elements manually.
- Once labeled, the dataset is ready for model training.
- Annotating 50,000 images can take 12,500 hours, emphasizing the need for a more efficient approach.
Automated data annotation
Automatic data labeling models are gaining popularity for faster data annotation. These models use predefined rules to assign labels to data, but they struggle when data structures change frequently. Combining human intelligence with AI can ensure more accurate results. Humans validate and optimize the AI’s decisions, while the AI handles the labeling process.
The Key benefits of data annotation
- Increased efficiency: Data annotation enhances machine learning systems, enabling them to better recognize objects, words, sentiment, intent, etc., thus improving efficiency.
- Greater precision: Accurate data labeling results in more precise data for algorithm training, leading to improved data extraction accuracy in the future.
- Reduced human intervention: Improved data annotation yields better AI model outputs, reducing the need for human intervention, thereby cutting costs and saving time.
- Scalability: Automated data annotation facilitates scaling of data annotation projects, enhancing AI and ML model improvement.
Why do you need data annotation?
Innovation relies on data annotation to teach machines about the world. For instance, self-driving cars need annotated data to recognize vehicles, streets, and pedestrians to avoid accidents. Similarly, businesses use annotated data to train AI models to categorize documents like invoices, streamlining workflows and saving time. This process helps machines understand and classify different types of documents, freeing up human workers from manual sorting tasks.
The Key Benefits of data annotation
- Enhanced Efficiency: Data labeling enhances the training of machine learning systems, improving their ability to recognize various elements such as objects, words, sentiment, and intent. This, in turn, boosts efficiency.
- Improved Precision: Accurate data labeling results in higher-quality data for training algorithms, leading to increased accuracy in data extraction for future use.
- Decreased Human Intervention: Better data annotation leads to improved AI model outputs, reducing the need for human involvement. This decrease in human intervention saves time and lowers costs.
- Scalability: Automated data annotation allows for the expansion of data annotation projects, facilitating the enhancement of AI and ML models on a larger scale.
Conclusion
Data annotation serves as the cornerstone for the advancement of AI and machine learning technologies. By meticulously labeling data, we empower these systems to understand and interpret the world around them, leading to groundbreaking innovations such as self-driving cars and intelligent document classification. With its ability to enhance efficiency, precision, and scalability while reducing human intervention, data annotation paves the way for a future where machines seamlessly integrate into various aspects of our lives, revolutionizing industries and driving progress forward.
