Image annotation is the process of labeling or tagging images. It is used to train AI models to recognise objects, people, or patterns. This technique is widely used in self-driving cars, medical imaging, and e-commerce.
As a business owner, you must have started using AI in your business operations. In fact, around 77% of companies are either using or exploring AI, and 64% of business owners believe AI will improve customer relationships.
But, is AI safe? Yes, a trained AI model is safe to use for business operations.
So, how are AI models trained? Is there any special training technique?
Yes, image annotation is one of them. In this technique, an AI model is trained on different images that are accurately labeled or tagged.
Want to know more about what photo annotation and evaluation is? In this article, let’s understand what image annotation is, its various types, and working.
What is Photo Annotation?
Photo or image annotation is the process of adding labels or tags to images. Such tagging allows computers to understand them. This process finds its application in artificial intelligence (AI) and machine learning (ML). Labeling is used to train AI models to recognise objects, people, or patterns in images (just like humans do). Mostly, image annotation services are offered by AI training companies.
For example,
- Say you are training a computer to recognise cats. Now, you would provide several images of cats with a label saying “cat.” Over time, the computer learns to identify cats in new images, even if they are different from the ones it has seen before.
Be aware that good image annotation is important because it helps AI make accurate decisions. If the labels are correct, the AI model will recognise things precisely. But if the labels are wrong or unclear, the AI might get confused and make mistakes.
What are the Different Types of Image Annotation?
Image annotation comes in different types. Each type serves a unique purpose in training AI models. By choosing the right annotation method, image annotation service providers can better train an AI model.
Below are the five key types of image annotation:
1. Bounding Box Annotation
In bounding box annotation, the AI companies mark objects in an image by drawing a rectangle around it (covering the object’s height and width).
This method is useful when the object doesn’t need precise shape detection, just a general location. It’s commonly used in applications like:
- Self-driving cars (to detect pedestrians and vehicles)
- E-commerce (to identify products in images)
Though it’s fast and easy, it is ideal for objects with irregular shapes like animals or curved objects.
2. Polygon Annotation
When objects have complex shapes, simple rectangles don’t work well. Here, polygon annotation is used to mark the exact edges of an object by connecting multiple points. This makes detection more accurate for irregular objects like:
- Traffic signs
- Logos
- Human faces
This technique is widely used in autonomous driving, medical imaging, and aerial photography.
3. Cuboid Annotation
Cuboid annotation is a 3D version of bounding box annotation. Instead of just marking height and width, it also includes depth. This helps AI understand how far an object is in a 3D space.
This image annotation technique is used in:
- Self-driving technology
- Construction planning
- Robotics
For example,
- It helps autonomous vehicles estimate the distance between a car and a pedestrian.
- In medical imaging, it helps in identifying the size and shape of organs or tumors.
4. Text Annotation
Text annotation is used to teach AI how to understand and interpret text in images and documents. To annotate an image, words, phrases, or sentences are marked. This marking is done to indicate their:
- Meaning
- Sentiment
- Intent
This technique is useful for chatbots, voice assistants, and document processing.
For example,
- An AI model trained on text annotation can understand customer emotions in online reviews. It can extract key information from scanned invoices.
5. Semantic Segmentation
Semantic segmentation breaks an image into different sections. After this, it assigns a category to each pixel. Instead of just marking objects, it classifies every part of the image. Some common applications of this image annotation technique are:
- In a self-driving car, semantic segmentation distinguishes between roads, pedestrians, vehicles, and traffic signs at a pixel level.
- In medical imaging, it is used to detect tumors or tissues.
- In agriculture, it is used to separate crops from soil.
How Does Image Annotation Work?
To label images, most image annotation service providers need:
- An image annotation tool
- Good-quality training data
Let’s see how image annotation is done using a combination of these two:
1. Source Raw Image or Video Data
The first step is to collect the images or videos that need to be annotated. This data is cleaned to remove low-quality images and duplicates before annotation begins.
AI training companies collect their own data or use publicly available datasets (some of which already have basic labels). High-quality data ensures better AI training. This makes the final model more accurate in recognising objects and patterns.
2. Find Out What Label Types To Use
Different AI tasks require different types of annotations. For example:
- Say an AI company is training a model for image classification. They label images with simple categories like “cat” or “dog.”
- For object detection, they use bounding boxes to mark objects.
- For image segmentation, more detailed annotations like masks or polygons are needed.
Thus, by choosing the right label type, AI training companies ensure that their AI model understands images correctly and provides reliable results when processing new images.
3. Create a Class for Each Object to be Labeled
AI models work best when trained with a consistent set of labels. Before starting annotation, most companies define a list of object classes (e.g., car, person, tree). This allows every similar object to get the same label.
Some annotation tools even come with:
- Predefined class sets
- Color coding
This prevents confusion, duplicate classes, and mislabeling.
4. Annotate with the Right Tools
Once the class labels are set, the image annotation service providers begin the actual annotation process. Depending on the task, they draw:
- Bounding boxes
- Polygons
- Segment masks around objects
If the annotation is simple, they just tag an image with labels. Some advanced annotation tools, like V7 automate and speed up this process. This leads to fewer errors and makes the data more usable for training.
5. Version The Dataset and Export It
After annotating, the data must be stored in the right format. Some common export formats include:
- JSON
- XML
- COCO
- Pascal VOC
Properly formatted data allows the AI to start learning immediately without extra processing.
How Do Image Annotation Service Providers Obtain High Quality Data in Bulk?
AI models perform better when they are trained on bulk quality data. A recent study showed that if a model is trained on only 100 transactions, it may not learn enough patterns to make accurate predictions. However, with 10,000 transactions, the model can better recognise trends and reduce errors.
Thus, both quality and quantity are important. Let’s check out three primary sources exploited by most image annotation service providers to get high-quality image data:
1. Open Datasets
Open datasets are freely available image collections created by researchers. Some common examples of these datasets are:
- ImageNet
- COCO
- Places365
These datasets contain millions of labeled images and are widely used for AI training. However, they are mostly for academic research and often come with restrictions on commercial use.
2. Self-Annotated Data
If open datasets don’t fully meet the needs, some image annotation service providers collect and label their own data. Usually, they do so by taking photos using cameras, drones, or medical scanners. Some even download images from free-to-use image websites (like Unsplash, Wikimedia, or Creative Commons).
3. Web Scraping
Web scraping is a technique that uses scripts to automatically collect images from the internet. This collection is undertaken based on specific keywords. It is a quick way to get images on a specific topic, such as cars, animals, or products. Most companies use web scraping when they need large amounts of image data quickly.
However, these images are often unorganised and require cleaning before image annotation.
Use Accurate AI Models, Enhanced by Image Annotation!
Recently, a study predicted that the global AI training dataset market was valued at USD 2.60 billion in 2024. It is projected to grow at a CAGR of 21.9% from 2025 to 2030 and reach an impressive USD 8.60 billion by 2030.
This clearly shows that AI training is an important part of AI development. It is widely done through image annotation, which allows AI models to recognise patterns, objects, and text with precision. Most image annotation service providers use techniques like bounding box annotation, semantic segmentation, and text labeling to annotate images properly.
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FAQs on What is Photos Annotation and Evaluation
1. How does image annotation improve AI performance?
Properly labeled images allow AI to:
- Learn patterns
- Reduce errors
- Make smarter predictions
Also, when trained on high-quality image annotation, the accuracy of AI models increases in facial recognition and product categorisation.
2. What industries benefit the most from image annotation?
Some common beneficiaries are industries like:
- E-commerce (product tagging)
- Healthcare (medical imaging)
- Automotive (self-driving cars)
- Security (facial recognition)
Companies operating in these sectors heavily rely on image annotation to enhance automation and efficiency.
3. Can image annotation help improve customer experience (CX)?
Yes, by using properly trained AI models on high-quality image annotation, you can personalise customer interactions and improve search accuracy. Also, you can enhance automation in customer support, which leads to better CX and engagement.
4. What are the challenges in image annotation?
The biggest challenges are:
- Data quality
- Consistency in labeling
- The high cost of manual annotation
However, most image annotation service providers use automated tools to reduce these difficulties.
5. Is image annotation expensive?
Generally, costs depend on the complexity and volume of images. Small AI companies usually start with open datasets and automation tools. Some even use affordable annotation services to scale gradually.
