YOGA: Deep Object Detection in the Wild with Lightweight Feature
  Learning and Multiscale Attention
Raja Sunkara Tie Luo
We introduce YOGA, a deep learning based yet lightweight object detection
model that can operate on low-end edge devices while still achieving
competitive accuracy. The YOGA architecture consists of a two-phase feature
learning pipeline with a cheap linear transformation, which learns feature maps
using only half of the convolution filters required by conventional
convolutional neural networks. In addition, it performs multi-scale feature
fusion in its neck using an attention mechanism instead of the naive
concatenation used by conventional detectors. YOGA is a flexible model that can
be easily scaled up or down by several orders of magnitude to fit a broad range
of hardware constraints. We evaluate YOGA on COCO-val and COCO-testdev datasets
with other over 10 state-of-the-art object detectors. The results show that
YOGA strikes the best trade-off between model size and accuracy (up to 22%
increase of AP and 23-34% reduction of parameters and FLOPs), making it an
ideal choice for deployment in the wild on low-end edge devices. This is
further affirmed by our hardware implementation and evaluation on NVIDIA Jetson
Nano.