Most object detection frameworks use backbone architectures originally
designed for image classification, conventionally with pre-trained parameters
on ImageNet. However, image classification and object detection are essentially
different tasks and there is no guarantee that the optimal backbone for
classification is also optimal for object detection. Recent neural architecture
search (NAS) research has demonstrated that automatically designing a backbone
specifically for object detection helps improve the overall accuracy. In this
paper, we introduce a neural architecture adaptation method that can optimize
the given backbone for detection purposes, while still allowing the use of
pre-trained parameters. We propose to adapt both the micro- and
macro-architecture by searching for specific operations and the number of
layers, in addition to the output channel dimensions of each block. It is
important to find the optimal channel depth, as it greatly affects the feature
representation capability and computation cost. We conduct experiments with our
searched backbone for object detection and demonstrate that our backbone
outperforms both manually designed and searched state-of-the-art backbones on
the COCO dataset.

本文提出了一种神经架构适应方法，可优化特定的骨干网络以适用于对象检测，同时仍可使用预训练参数。通过搜索特定运算和每个块的输出通道维度，我们旨在适应微观和宏观架构。实验证明，在 COCO 数据集上，我们搜索的骨干网表现出优异性能，超过了手工设计和搜索的最先进骨干网。

搜索通道维度和映射预训练参数进行目标检测的神经体系结构适应

Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters

In object detection, the detection backbone consumes more than half of the
overall inference cost. Recent researches attempt to reduce this cost by
optimizing the backbone architecture with the help of Neural Architecture
Search (NAS). However, existing NAS methods for object detection require
hundreds to thousands of GPU hours of searching, making them impractical in
fast-paced research and development. In this work, we propose a novel zero-shot
NAS method to address this issue. The proposed method, named MAE-DET,
automatically designs efficient detection backbones via the Maximum Entropy
Principle without training network parameters, reducing the architecture design
cost to nearly zero yet delivering the state-of-the-art (SOTA) performance.
Under the hood, MAE-DET maximizes the differential entropy of detection
backbones, leading to a better feature extractor for object detection under the
same computational budgets. After merely one GPU day of fully automatic design,
MAE-DET innovates SOTA detection backbones on multiple detection benchmark
datasets with little human intervention. Comparing to ResNet-50 backbone,
MAE-DET is $+2.0\%$ better in mAP when using the same amount of
FLOPs/parameters, and is $1.54$ times faster on NVIDIA V100 at the same mAP.
Code and pre-trained models are available at
this https URL

该研究提出了一种名为 MAE-DET 的零样本神经架构搜索（NAS）方法，其通过最大熵原理自动设计高效的目标检测骨干网络，仅需一天的 GPU 时间即可在多个检测基准数据集上提供 SOTA 检测骨干网络，可显着降低架构设计成本并提高检测性能。