  Transformers are a popular choice for classification tasks and as backbones
for object detection tasks. However, their high latency brings challenges in
their adaptation to lightweight object detection systems. We present an
approximation of the self-attention layers used in the transformer
architecture. This approximation reduces the latency of the classification
system while incurring minimal loss in accuracy. We also present a method that
uses a transformer encoder layer for multi-resolution feature fusion. This
feature fusion improves the accuracy of the state-of-the-art lightweight object
detection system without significantly increasing the number of parameters.
Finally, we provide an abstraction for the transformer architecture called
Generalized Transformer (gFormer) that can guide the design of novel
transformer-like architectures.
