Boost Annotation Speed 5x With Model Accelerated Annotation

When building computer vision applications, the quality of your data is everything. The success of your models relies heavily on data annotation - the process of labeling images so a model can learn to recognize objects, actions, or scenarios in new data. However, with the increasing demand for high-quality datasets in computer vision, traditional data annotation methods can be incredibly time-consuming and resource-intensive. Recognizing this challenge, I decided to test model accelerated annotation with semi-supervised learning to demonstrate how much faster and more efficient it is compared to manual annotation.

So, what exactly is model accelerated annotation? It is a technique that assists with the annotation process by leveraging advanced machine learning methods. For this test, I implemented model accelerated annotation in conjunction with semi-supervised learning. I first trained the initial model on a small labeled dataset. Then, once it was trained, it began generating predictions or annotations for the remaining unlabeled data and sent them to the user for guidance.

Essentially, with model accelerated annotation the user’s task shifts from manually generating annotations to simply validating suggestions from the model - saving time and effort. 

What Is Computer Vision?

In simple terms, computer vision enables machines to see and interpret visual data from the world around them. From facial recognition systems to autonomous vehicles, computer vision powers applications that rely on analyzing images or video to extract meaningful information. These systems need to be trained on large amounts of labeled data, which is where data annotation comes into play.

What Is Data Annotation?

Data annotation is the process of labeling images so that machine learning models can understand what they’re looking at. For example, in a dataset of images from a retail store, you might label each image with tags like “person,” “employee,” or “food item.” This helps the model eventually learn to identify these objects or people in real-world settings. For a production quality model, the amount of images required can be in the hundreds of thousands. So, manually annotating a dataset of that size is extremely slow and resource-intensive.

Streamlining the Annotation Process

Imagine if you didn’t have to manually label every image yourself; instead only labeling a few images from your dataset and then training a model to assist you by suggesting annotations for the rest of the images. That’s what model accelerated annotation paired with semi-supervised learning does!

The approach transforms the task from full manual annotation to a simpler validation process, where users primarily focus on validating the model's suggestions. This drastically speeds up the workflow, allowing you to annotate datasets much more efficiently.

Methods

To put model accelerated annotation with semi-supervised learning to the test, I conducted experiments using three different datasets. Each one represented a unique annotation challenge, allowing me to assess its speed and efficiency across various use cases. Here’s a breakdown of the datasets:

1. Restaurant Franchise Dataset
   • Size: 462 images
   • Classes: employee, food item
   • Average Annotations per Image: 3
   • Images: Captured from a fixed angle within a restaurant setting.
2. Retail Store Customer Dataset
   • Size: 2,563 images
   • Class: person
   • Average Annotations per Image: 5.3
   • Images: Captured of customers near the shop doorway, featuring four different angles and environments.
3. Diverse Object Dataset
   • Size: 11,707 images
   • Classes: person, car, bus, airplane, boat, drone
   • Average Annotations per Image: 4
   • Images: A varied collection, mimicking a random sample of objects from Google Images.

These datasets were selected to test model accelerated annotation with semi-supervised learning across different environments, annotation complexities, and image quantities, with the goal of evaluating how much time could be saved using this method.

First, I manually annotated a sample from all the datasets to get a baseline annotation speed for each. These values varied due to factors like the number of classes and complexity of images. 

Next, I ran each dataset through alwaysAI’s model accelerated annotation tool. I simply annotated an initial count of images, in my case 50, then kicked off the semi-supervised learning session. The system would train a model, analyze the performance of that model, use it to inference against the remaining images, and finally present the results back to me. I cycled through this process until every image was annotated, which in the case of the largest dataset, was 5000 images.

Model Accelerated Annotation Features

The model accelerated annotation tool only requires a small amount of input from the user (semi-supervised), and it receives that input by presenting the results of the model to the user in one of two ways:

1. Annotation Recommendations These are suggested annotations on images where the data suggests the model’s performance is weaker. The tool provides predictive suggestions for labeling objects, which the user can validate. Although the predictions are not always perfect, they significantly speed up the annotation process.
2. Validation Recommendations These are images where analysis predicts the model is performing well, making validation quick and easy. You can simply accept or reject the model's suggestions and add validated images to the training set. The ability to sort images by similarity further accelerates the process when working with large batches of similar images.

With annotation recommendations, you can boost the model's accuracy, while with validation recommendations, you can increase confidence in the model’s predictions as the session progresses.

Results

Throughout the annotation process, I tracked the number of annotated images at different stages to measure the speed improvements from model accelerated annotation and semi-supervised learning. Here’s a breakdown of the results:

Chart showcasing how much time is saved with alwaysAI's model accelerated annotation with semi-supervised learning.

Graph showcasing how much time is saved with alwaysAI's model accelerated annotation with semi-supervised learning.

Although the experiment wasn’t controlled in a highly scientific manner, the results clearly show that model accelerated annotation with semi-supervised learning can boost annotation speed dramatically, allowing you to get through large datasets much quicker than through manual efforts alone.

Use Cases for Model Accelerated Annotation With Semi-supervised Learning

The model accelerated annotation with semi-supervised learning method is designed to be flexible enough to accelerate a wide variety of annotation tasks. Here are a few specific use cases where it can be particularly helpful:

• Adding New Object Classes: Model accelerated annotation with semi-supervised learning allows you to quickly expand your dataset with new objects by streamlining the annotation process for these new classes, using a small labeled dataset to generate accurate suggestions.
• Incorporating Different Angles or Environments: When your dataset involves varied camera angles or environments, semi-supervised learning helps the model efficiently adapt to these variations and assists in annotating them more effectively.
• Addressing Model Weaknesses: If your model struggles with particular images or classes, model accelerated annotation with semi-supervised learning can quickly identify and help you annotate these problematic areas by generating improved predictions for the unlabeled data.
• Annotating a Dataset From Scratch: Starting a new dataset? Model accelerated annotation combined with semi-supervised learning allows you to build an initial model early on with minimal manual input and start benefiting from its annotation suggestions almost immediately.

Conclusion

Reflecting on the early days at alwaysAI, building datasets often required an “all hands on deck” approach, with team members from multiple departments pitching in to meet tight deadlines. While this strategy got the job done, it was slow, costly, and diverted resources from their core responsibilities. Onboarding multiple people also proved inefficient, especially when training new team members on specific classes and edge cases. The process of teaching these nuances made scaling the annotation team more of a burden than a solution.

Model accelerated annotation with semi-supervised learning changes the game. Now, one person or a small, focused team can produce high-quality datasets at a fraction of the time and cost. This eliminates the need for extensive onboarding and misallocation of resources, making the process leaner and more efficient. What excites me most is how this method allows you to observe the model evolving over time as more images are added, opening up new insights into model performance and optimization. Plus, it makes it easier to scale your annotation efforts as your business grows, enabling you to seamlessly expand your vision AI capabilities across multiple projects or locations.

With model accelerated annotation and semi-supervised learning, the process of annotating datasets has become faster, smarter, and more efficient than ever before. Ready to speed up your annotation workflow? Speak with an AI Expert to explore model accelerated annotation today!