Build and learn

December 12, 2024

Master data annotation: Tools, tips, and best practices

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Sara Gharnit
Senior Content Marketer

Data annotation might sound technical, but it’s a cornerstone of artificial intelligence (AI) and machine learning (ML). Without it, AI models can’t learn or improve.

Think of it as teaching a machine to differentiate between a mushroom and a piece of pepperoni on a pizza: annotation is an integral step in the process that makes it possible.

At Viam, we take a practical approach to data annotation, building tools that simplify the process and empower developers to create smarter systems. In this blog, we’ll break down the basics of data annotation, why it’s essential, the challenges it presents, and how Viam helps users tackle them.

What is data annotation?

Data annotation is the process of labeling or tagging raw data to provide context. If you’ve ever searched your phone for a photo of a specific location or a friend’s face, you’ve seen annotations in action. For instance, metadata like time, location, or objects in an image help your phone sort and find photos.

In ML, annotation is more structured. It involves tools like bounding boxes, which outline objects in an image, or labels that categorize data. These annotations allow machines to recognize and classify patterns, objects, or behaviors.

As one Viam engineer explained, “Annotations are like telling the model the truth—it learns what we teach it.”

Why is data annotation essential?

Annotations serve two critical purposes in ML:

1. Organization: Annotations help group data into meaningful datasets. For example, if you’re collecting sensor data across several experiments, some may include faulty readings due to hardware malfunctions. Annotations can exclude these noisy datasets and highlight high-quality data.
2. Training: Annotations act as the foundation for model training. For example, labels on an image can teach a model to identify specific objects, like distinguishing between a mushroom and a piece of pepperoni on a pizza.

This dual role—organizing and training—makes data annotation a non-negotiable step in ML workflows.

Types of data annotation

At Viam, our platform supports three primary types of data annotation:

Image annotation

This includes tools for object detection and classification.

• Object detection: This process involves identifying and locating multiple objects within an image. By drawing bounding boxes around each object, the model learns not only what the objects are but also where they are situated within the image. For instance, in a street scene, object detection can identify and locate cars, pedestrians, and traffic lights, providing precise coordinates for each.

• Image classification: This task assigns a label to an entire image based on its predominant content. Unlike object detection, image classification doesn't specify object locations but determines the main subject of the image. For example, an image classification model might analyze a picture and classify it as containing a dog or a cat, without indicating their positions within the image.

Video annotation

Viam enables video annotation by allowing developers to apply the same data annotation techniques used for static images to videos. This supports tasks like tracking objects across frames or annotating recurring patterns.

Video annotation is particularly valuable for applications such as traffic monitoring, object detection in dynamic environments, and more. By correlating objects between frames, Viam provides tools for training models that can recognize continuity and movement.

For example, our Principal Solutions Engineer created an object detection model to identify whether workers on a construction site were following safety protocols by wearing helmets.

Sensor data annotation

This is particularly relevant for IoT and smart machine applications. Viam’s tools allow developers to annotate physical environment data, such as temperature readings or object localization.

While these are our focus today, Viam’s modular platform is adaptable, making it possible to extend annotation support to other areas in the future.

How data annotation supports machine learning

Improving training workflows

Annotations make ML training more efficient and accurate. High-quality annotations ensure models are learning from clean and reliable datasets, reducing errors during deployment.

Take a common example from Viam: in the food processing industry, engineers annotate pizza images with bounding boxes to identify toppings. These annotations feed into models that optimize sorting processes, reducing time and waste.

Addressing noisy data

Another common use case is managing faulty sensor data. Say an experiment outputs inaccurate temperature readings due to sensor issues. Annotations can flag these readings, ensuring they don’t disrupt model training.

The challenges of data annotation

Time-intensive workflows

Let’s face it—data annotation can be a time-intensive process. Manually labeling thousands of images or datasets demands significant effort, and for teams on tight deadlines, it can quickly become overwhelming. Viam streamlines this process with:

• User-friendly tools: Intuitive interfaces for drawing annotations, such as labeling an image with a bounding box directly within the Viam app.

• Auto-labeling: Viam supports workflows that integrate Vision Language Models (VLMs) and grounding models as part of a “two-phase” process.

Scaling annotations across datasets

As your dataset grows, managing annotations becomes exponentially harder. Without the right tools, you risk creating bottlenecks that slow your workflow. Enter Viam:

• Integrated platform: Annotate, train, and deploy—all in one seamless ecosystem. No need to jump between tools, and no extra complexity to manage.
• Effortless scalability: Whether you’re working with a small dataset or scaling to thousands of data points, Viam’s tools grow with your needs.

Best practices for effective data annotation

The better your data annotations are, the better your model performs. Here are some ways to build a workflow that’s consistent, efficient, and accurate:

• Set clear guidelines: Consistency is everything in data annotation. Determine whether to annotate only primary objects of interest or include secondary objects. Having clear rules minimizes confusion and keeps your annotations uniform across the dataset.
• Leverage automation: Advanced tools can provide a head start on manual labeling. Automating this process can enhance efficiency.
• Conduct quality checks: Regularly review your annotations to catch errors or inconsistencies.
• Diversify your dataset: Ensure your data reflects a wide range of conditions.
• Include negative examples: Teaching your model what isn’t important is just as valuable.
• Balance your data: Make sure each class in your dataset has equal representation.

Build a data annotation pipeline

Viam simplifies creating and managing annotations for training ML models. Whether you're working with images, video frames, or sensor data, our intuitive platform streamlines the process from start to finish.

Step 1 - Prepare your dataset

Start by collecting diverse, high-quality data. Use Viam’s tools to upload images, videos, or sensor data.

Step 2 - Annotate your data

Label your dataset using Viam’s annotation tools. Consistency and diversity in your labels are key to ensuring robust model performance.

Step 3 - Train your model

Train your ML model directly on Viam’s platform by selecting your dataset, defining the task type, and starting the training process.

Step 4 - Deploy and refine

Deploy your model in minutes and monitor its performance. Continue refining your dataset and annotations as needed.

Why Viam stands out for data annotation

Viam’s platform is designed to support your data annotation journey, whether you’re labeling images, video frames, or sensor data. Here’s how:

1. Ease of use: Viam’s tools are designed for efficiency, allowing users to annotate quickly and accurately.
2. Integrated ecosystem: Annotate, train, and deploy—all within the same platform. This end-to-end approach ensures consistency.
3. Real-world applications: Viam’s tools address practical challenges faced by engineers and developers.