  Model compression and hardware acceleration are essential for the
resource-efficient deployment of deep neural networks. Modern object detectors
have highly interconnected convolutional layers with concatenations. In this
work, we study how pruning can be applied to such architectures, exemplary for
YOLOv7. We propose a method to handle concatenation layers, based on the
connectivity graph of convolutional layers. By automating iterative sensitivity
analysis, pruning, and subsequent model fine-tuning, we can significantly
reduce model size both in terms of the number of parameters and FLOPs, while
keeping comparable model accuracy. Finally, we deploy pruned models to FPGA and
NVIDIA Jetson Xavier AGX. Pruned models demonstrate a 2x speedup for the
convolutional layers in comparison to the unpruned counterparts and reach
real-time capability with 14 FPS on FPGA. Our code is available at
https://github.com/fzi-forschungszentrum-informatik/iterative-yolo-pruning.
