## 1 Introduction  
Machine learning has achieved great success in various tasks, including computer vision [1; 2; 3], nature language processing [4; 5], and digital image and signal processing [6; 7; 8]. Neural architecture search aims to automatically find the best neural network architecture in some search space instead of manually design based on a large amount of trails and expert knowledge. Several approaches for NAS have been explored, including reinforcement learning, evolutionary algorithm, and differentiable NAS (DARTS). The works [9; 10; 11; 12] adopt reinforcement learning by considering the generation of an architecture as the agent's action. Another reinforcement learning based method [13], named ENAS, proposes weight sharing strategy among the same operations in different architectures, which can significantly reduce the time cost during the search process. Another work [14] adopts maximum flow in graph theory to address NAS task. Other works [15; 16; 17; 18; 19; 20] adopt genetic algorithms by first encoding the neural architecture and then proposing a group of architectures as a population. Population of architectures are selected according to their performance and then new individuals are generated by crossover and mutation strategies. Apart from the above methods, one-shot neural architecture search [21] has been one of the most popular searching paradigms of neural architecture search (NAS) for its high efficiency. Unlike reinforcement-learning-based methods [9; 10; 11; 12] and evolutionary algorithms [15; 16; 17; 18; 19; 20] that generate one candidate network at a time, one-shot-based methods construct a supernet containing all the connections in the search space and jointly train all operation weights. DARTS [22] further introduces architecture parameters and address NAS as a bi-leveloptimization problem which is solved by stochastic gradient descent (SGD). After the gradient-based search phase, DARTS discretizes the supernet and infers the final architecture simply according to the value of architecture parameters, which is referred to as the discretization procedure. Some works [23; 24] notice the huge memory cost of one-shot NAS and are dedicated to more efficient searching methods. Moreover, the works [25; 26] attempt to improve the optimization algorithm for more stable searching. Differentiable architecture search [22] has taken the dominance with a myriad of follow-up works [26; 27; 28; 29; 30] to reduce the search cost. However, for object detection task, due to the complexity of network and difficult to train models, the above methods that focus on classification task can not directly search on object detection dataset. Unlike classification models, object detection models contain two modules: backbone and feature pyramid network (FPN). To reduce the search cost, recent works only search for the architectures of backbone [31; 32] or FPN [33; 34; 35] separately. This problem has aroused widespread concern in the industry, because in practice, manually adjusting each module based on the standard detection model is inefficient and suboptimal. It is difficult to use and evaluate the trade-off between reasoning time and accuracy and the presentation ability of each module in different datasets. In particular, authors in SM-NAS [36] find that \"the combination of cascade RCNN and resnet18 (not the standard detection model) is faster and more accurate than the combination of FPN and resnet50 in coco [37] and BDD [38] (automatic driving data set)\". Though many prior works have been dedicated to exploring neural architecture search for object detection tasks, they suffers vast GPU memory cost and searching time, e.g., 44 GPU-days for DetNAS [31]. The above NAS methods for object detection adopt RetinaNet and Faster-RCNN framework. However, few NAS methods adopt YOLO framework [39; 40; 41; 42], which are more efficient detector. EAutoDet [43] proposes an efficient architecture search method for YOLO framework and is able to discover effective architectures in a few GPU-days.