Dataset Culling: Towards Efficient Training Of Distillation-Based Domain Specific Models
This addresses the training bottleneck for real-time surveillance applications, offering a significant efficiency improvement.
The paper tackles the problem of high training costs for domain-specific object detection models by proposing Dataset Culling, which filters easy-to-classify images to reduce dataset size, achieving a 300x reduction in dataset size and 47x faster training with no accuracy loss.
Real-time CNN-based object detection models for applications like surveillance can achieve high accuracy but are computationally expensive. Recent works have shown 10 to 100x reduction in computation cost for inference by using domain-specific networks. However, prior works have focused on inference only. If the domain model requires frequent retraining, training costs can pose a significant bottleneck. To address this, we propose Dataset Culling: a pipeline to reduce the size of the dataset for training, based on the prediction difficulty. Images that are easy to classify are filtered out since they contribute little to improving the accuracy. The difficulty is measured using our proposed confidence loss metric with little computational overhead. Dataset Culling is extended to optimize the image resolution to further improve training and inference costs. We develop fixed-angle, long-duration video datasets across several domains, and we show that the dataset size can be culled by a factor of 300x to reduce the total training time by 47x with no accuracy loss or even with slight improvement. Codes are available: https://github.com/kentaroy47/DatasetCulling