F2Zip: Finetuning-Free Model Compression for Scenario-Adaptive Embedded Vision

With the development of the Internet of Things and artificial intelligence, the deployment and inference of intelligent models have gradually raised concerns. To reduce the huge computation and storage overhead of modern deep neural networks, many studies use model pruning techniques to reduce the model size and computational cost. However, existing pruning techniques usually require model fine-tuning, which incurs high additional overhead, making them difficult to apply to real-world scenarios. In this work, we focus on vision model compression and present F2Zip, a scenario-adaptive finetuning-free pruning framework for embedded devices. First, we propose a scenario complexity measurement that quantifies scenario changes with pixel-level entropy. By analyzing the scenario complexity, F2Zip adaptively evaluates the importance of different channels and layers of the model using only a small amount (tens) of unlabeled data. Then we design a multi-constraint knapsack solver to prune scenario-unrelated redundant channels. We implemented and deployed F2Zip in surveillance scenarios and tested different models on videos collected from both public and real-world sources. Experimental results show that F2Zip is free of model fine-tuning in various scenarios. F2Zip reduces the end-to-end deployment time by 89.8% and reduces energy cost by 79.5%, which shows that F2Zip is computationally friendly for embedded devices. Without fine-tuning and any accuracy degradation, F2Zip achieves up to 50.2% parameter reduction, outperforming baseline methods by 35.1%.