Electrical engineering

Physics

Architecture

Pattern recognition (psychology)

Feature (linguistics)

Latency (audio)

Algorithm

Optics

Fuse (electrical)

Pyramid (geometry)

Engineering

Visual arts

Philosophy

Artificial intelligence

Object detection

Computer science

Linguistics

Computation

Feature extraction

Computer vision

Scalability

Telecommunications

Database

Convolutional neural network

Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales. NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy with less computation time.

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection