The Big Data Myth: Using Diffusion Models for Dataset Generation to Train Deep Detection Models
This addresses the laborious data collection problem for researchers and practitioners in computer vision, offering a potential alternative, though it is incremental as it builds on existing diffusion models.
This study tackled the challenge of requiring extensive training data for deep object detection models by generating synthetic datasets using fine-tuned stable diffusion models, and found that models trained on this synthetic data performed similarly to a baseline trained on real-world images, with average precision deviations ranging from 0.09 to 0.12 in apple detection tasks.
Despite the notable accomplishments of deep object detection models, a major challenge that persists is the requirement for extensive amounts of training data. The process of procuring such real-world data is a laborious undertaking, which has prompted researchers to explore new avenues of research, such as synthetic data generation techniques. This study presents a framework for the generation of synthetic datasets by fine-tuning pretrained stable diffusion models. The synthetic datasets are then manually annotated and employed for training various object detection models. These detectors are evaluated on a real-world test set of 331 images and compared against a baseline model that was trained on real-world images. The results of this study reveal that the object detection models trained on synthetic data perform similarly to the baseline model. In the context of apple detection in orchards, the average precision deviation with the baseline ranges from 0.09 to 0.12. This study illustrates the potential of synthetic data generation techniques as a viable alternative to the collection of extensive training data for the training of deep models.