FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

June 1, 2020


Differentiable Neural Architecture Search (DNAS) has demonstrated great success in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS’s search space is small when compared to other search methods’, since all candidate network layers must be explicitly instantiated in memory. To address this bottleneck, we propose a memory and computationally efficient DNAS variant: DMaskingNAS. This algorithm expands the search space by up to 1014 × over conventional DNAS, supporting searches over spatial and channel dimensions that are otherwise prohibitively expensive: input resolution and number of filters. We propose a masking mechanism for feature map reuse, so that memory and computational costs stay nearly constant as the search space expands. Furthermore, we employ effective shape propagation to maximize per-FLOP or per-parameter accuracy. The searched FBNetV2s yield state-of-the-art performance when compared with all previous architectures. With up to 421× less search cost, DMaskingNAS finds models with 0.9% higher accuracy, 15% fewer FLOPs than MobileNetV3-Small; and with similar accuracy but 20% fewer FLOPs than Efficient-B0. Furthermore, our FBNetV2 outperforms MobileNetV3 by 2.6% in accuracy, with equivalent model size. FBNetV2 models are open-sourced at

Download the Paper


Written by

Alvin Wan

Xiaoliang Dai

Peizhao Zhang

Zijian He

Yuandong Tian

Saining Xie

Bichen Wu

Matthew Yu

Tao Xu

Kan Chen

Peter Vajda

Joseph E. Gonzalez


Conference on Computer Vision and Pattern Recognition (CVPR)

Research Topics

Computer Vision

Related Publications

June 17, 2019


DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition | Facebook AI Research

Motion has shown to be useful for video understanding, where motion is typically represented by optical flow. However, computing flow from video frames is very time-consuming. Recent works directly leverage the motion vectors and residuals…

Zheng Shou, Xudong Lin, Yannis Kalantidis, Laura Sevilla-Lara, Marcus Rohrbach, Shih-Fu Chang, Zhicheng Yan

June 17, 2019

June 18, 2019


Embodied Question Answering in Photorealistic Environments with Point Cloud Perception | Facebook AI Research

To help bridge the gap between internet vision-style problems and the goal of vision for embodied perception we instantiate a large-scale navigation task – Embodied Question Answering [1] in photo-realistic environments (Matterport 3D). We…

Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, Dhruv Batra

June 18, 2019

June 11, 2019



Adversarial Inference for Multi-Sentence Video Description | Facebook AI Research

While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the…

Jae Sung Park, Marcus Rohrbach, Trevor Darrell, Anna Rohrbach

June 11, 2019

June 10, 2019



Mixture Models for Diverse Machine Translation: Tricks of the Trade | Facebook AI Research

Mixture models trained via EM are among the simplest, most widely used and well understood latent variable models in the machine learning literature. Surprisingly, these models have been hardly explored in text generation applications such as…

Tianxiao Shen, Myle Ott, Michael Auli, Marc'Aurelio Ranzato

June 10, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.