· Data Annotations · 2 min read
Smart Annotation: Using Active Learning to Label Less
You don't need to label everything. Learn how Active Learning helps you identify the most valuable data points to annotate, saving time and money.
Labelling data is expensive. If you have 1 million unlabelled images, labelling all of them might cost £100,000. But here is the secret: your model probably already knows the answer to 90% of them.
What is Active Learning?
Active Learning is a loop where the model “asks” for help only when it is confused.
- Train: Train a model on a small seed dataset (e.g. 1,000 images).
- Inference: Run that model on the remaining 999,000 unlabelled images.
- Selection: Sort the results by “Confidence”. Ignore the ones where the model is 99% sure. Pick the ones where the model is 50/50 (uncertain).
- Label: Send only those confusing images to humans.
- Repeat: Add them to the training set and start again.
Why It Works
By focusing human effort on the “Edge Cases” (the weird lighting, the blurry objects, the rare angles), you improve the model much faster than by feeding it thousands of easy examples it already understands.
Saving 80% of the Budget
In our experience, Active Learning can achieve the same model performance with 20% of the data. That means you can either save 80% of your budget, or use that budget to build a model that is 5x better by covering more edge cases.
Stop wasting money on easy data. Let us design an Active Learning pipeline for you. Get in touch.
