  Computational efficiency in deep neural networks is critical for object
detection, especially as newer models prioritize speed over efficient
computation (FLOP). This evolution has somewhat left behind embedded and
mobile-oriented AI object detection applications. In this paper, we focus on
design choices of neural network architectures for efficient object detection
computation based on FLOP and propose several optimizations to enhance the
efficiency of YOLO-based models.
  Firstly, we introduce an efficient backbone scaling inspired by inverted
bottlenecks and theoretical insights from the Information Bottleneck principle.
Secondly, we present the Fast Pyramidal Architecture Network (FPAN), designed
to facilitate fast multiscale feature sharing while reducing computational
resources. Lastly, we propose a Decoupled Network-in-Network (DNiN) detection
head engineered to deliver rapid yet lightweight computations for
classification and regression tasks.
  Building upon these optimizations and leveraging more efficient backbones,
this paper contributes to a new scaling paradigm for object detection and
YOLO-centric models called LeYOLO. Our contribution consistently outperforms
existing models in various resource constraints, achieving unprecedented
accuracy and flop ratio. Notably, LeYOLO-Small achieves a competitive mAP score
of 38.2% on the COCOval with just 4.5 FLOP(G), representing a 42% reduction in
computational load compared to the latest state-of-the-art YOLOv9-Tiny model
while achieving similar accuracy. Our novel model family achieves a
FLOP-to-accuracy ratio previously unattained, offering scalability that spans
from ultra-low neural network configurations (< 1 GFLOP) to efficient yet
demanding object detection setups (> 4 GFLOPs) with 25.2, 31.3, 35.2, 38.2,
39.3 and 41 mAP for 0.66, 1.47, 2.53, 4.51, 5.8 and 8.4 FLOP(G).
