# NAS-FCOS: Fast Neural Architecture Search for Object Detection

arXiv: https://arxiv.org/abs/1906.04423 · HTML: https://mlsanity.com/paper/1906.04423/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 30 of 53)

- [RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://mlsanity.com/paper/2212.07784/) (2022)
- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [CBNet: A Composite Backbone Network Architecture for Object Detection](https://mlsanity.com/paper/2107.00420/) (2021)
- [Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images](https://mlsanity.com/paper/2303.14488/) (2023)
- [Core-set Sampling for Efficient Neural Architecture Search](https://mlsanity.com/paper/2107.06869/) (2021)
- [OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection](https://mlsanity.com/paper/2103.04507/) (2021)
- [WAS-VTON: Warping Architecture Search for Virtual Try-on Network](https://mlsanity.com/paper/2108.00386/) (2021)
- [G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection](https://mlsanity.com/paper/2402.04672/) (2024)
- [MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection](https://mlsanity.com/paper/2111.13336/) (2021)
- [DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network](https://mlsanity.com/paper/2303.02165/) (2023)
- [VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection](https://mlsanity.com/paper/2506.04623/) (2025)
- [Neural Architecture Search for Dense Prediction Tasks in Computer Vision](https://mlsanity.com/paper/2202.07242/) (2022)
- [FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors](https://mlsanity.com/paper/2303.08319/) (2023)
- [SliceMamba with Neural Architecture Search for Medical Image Segmentation](https://mlsanity.com/paper/2407.08481/) (2024)
- [Learning Versatile Neural Architectures by Propagating Network Codes](https://mlsanity.com/paper/2103.13253/) (2021)
- [RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving](https://mlsanity.com/paper/2108.08019/) (2021)
- [FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation](https://mlsanity.com/paper/2111.02394/) (2021)
- [ASFD: Automatic and Scalable Face Detector](https://mlsanity.com/paper/2201.10781/) (2022)
- [EAutoDet: Efficient Architecture Search for Object Detection](https://mlsanity.com/paper/2203.10747/) (2022)
- [Edge-guided Representation Learning for Underwater Object Detection](https://mlsanity.com/paper/2306.00440/) (2023)
- [3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification](https://mlsanity.com/paper/2101.04287/) (2021)
- [On the Privacy Risks of Cell-Based NAS Architectures](https://mlsanity.com/paper/2209.01688/) (2022)
- [Bag of Tricks for Neural Architecture Search](https://mlsanity.com/paper/2107.03719/) (2021)
- [CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search](https://mlsanity.com/paper/2107.03463/) (2021)
- [Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration](https://mlsanity.com/paper/2206.13962/) (2022)
- [Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"](https://mlsanity.com/paper/2302.00932/) (2023)
- [ELASTIC: Efficient Once For All Iterative Search for Object Detection on Microcontrollers](https://mlsanity.com/paper/2503.21999/) (2025)
- [Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2110.11084/) (2021)
- [Searching for TrioNet: Combining Convolution with Local and Global Self-Attention](https://mlsanity.com/paper/2111.07547/) (2021)
- [Training BatchNorm Only in Neural Architecture Search and Beyond](https://mlsanity.com/paper/2112.00265/) (2021)

## Abstract (arXiv)

The success of deep neural networks relies on significant architecture engineering. Recently neural architecture search (NAS) has emerged as a promise to greatly reduce manual effort in network design by automatically searching for optimal architectures, although typically such algorithms need an excessive amount of computational resources, e.g., a few thousand GPU-days. To date, on challenging vision tasks such as object detection, NAS, especially fast versions of NAS, is less studied. Here we propose to search for the decoder structure of object detectors with search efficiency being taken into consideration. To be more specific, we aim to efficiently search for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely FCOS, using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms and strategies for evaluating network quality, we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs. The discovered architecture surpasses state-of-the-art object detection models (such as Faster R-CNN, RetinaNet and FCOS) by 1.5 to 3.5 points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS for object detection.