We ran three experiments:
We created one copy of each original MSCOCO image in each of them.
Figure 2: Validation metrics obtained during the experiment with 25% subset of COCO dataset.
Purple curve is the baseline training (original images from COCO), blue curve - the training for the original images + images augmented with Kopikat. Vertical axis is Mean Average Precision (mAP) across different IoU thresholds ranging from 0.50 to 0.95. It is a commonly used metric for evaluating Object Detection models. We observed a consistent metric improvement in every epoch of the training: there was never a point when this dataset was worse than the original one.
Figure 3. The results of the experiments on 5%, 25%, and 100% COCO with Kopikat augmentations.
We observed an improvement in most of the experiments, but the improvement for smaller datasets was more significant than for 100% COCO. For a 5% split, we got a boost of 1.1 AP points, or 14.6%, right out of the box. For a 25% split, we got a boost of 1.6 AP points or 9.1%, and 0.42 AP, or 1.73%, for the full COCO.Our intuition is that smaller datasets lack the diversity needed to fully represent the use case and fight overfitting, while in big datasets, this issue is not as important. In real industrial applications, the typical dataset size is 5000 mages or smaller, so we believe Kopikat can be helpful for many projects.
Here are some examples of how Kopikat augments the data. Here we use images from COCO dataset and show their original annotation on both source and generated images. For every image, the first row is the original image, the second row - its Kopikat augmentation.
1. Object Detection:
Improving the accuracy of models such as YOLOX-Nano ensures more effective real-time object recognition. This is super useful in many products like retail traffic analysis, security systems, autonomous vehicles, and many other domains.
2. Neural Network Training with Limited Data:
Kopikat diversifies limited datasets and allows to use them in industrial applications. Small datasets are very common in real-life projects - and we hope to help build products even in these cases.
3. Transfer Learning:
Models trained on augmented data can be used for transfer learning to other tasks or datasets. This can significantly reduce the time and resources needed to train neural networks for new tasks from scratch.
Kopikat introduces a completely new approach that we call Generative Data Augmentation. Our experiments show its effectiveness for computer vision models.
Kopikat allows the user to enlarge and diversify datasets and is specifically helpful for datasets with up to 5,000 images that are typical for real-life AI projects. Data augmentation opens new opportunities for researchers and developers, providing tools to enhance model training. We plan to develop our product further, making it more effective and versatile.
We believe that the importance of data will only grow in the upcoming years, and we are ready to support this growth by offering innovative solutions.