Rethinking Vision Transformers for MobileNet Size and Speed

With the success of Vision Transformers (ViTs) in computer vision tasks, recent arts try to optimize the performance and complexity of ViTs to enable efficient deployment on mobile devices. Multiple approaches are proposed to accelerate attention mechanism, improve inefficient designs, or incorporate mobile-friendly lightweight convolutions to form hybrid architectures. However, ViT and its variants still have higher latency or considerably more parameters than lightweight CNNs, even true for the years-old MobileNet. In practice, latency and size are both crucial for efficient deployment on resource-constraint hardware. In this work, we investigate a central question, can transformer models run as fast as MobileNet and maintain a similar size? We revisit the design choices of ViTs and propose a novel supernet with low latency and high parameter efficiency. We further introduce a novel fine-grained joint search strategy for transformer models that can find efficient architectures by optimizing latency and number of parameters simultaneously. The proposed models, EfficientFormerV2, achieve 3.5% higher top-1 accuracy than MobileNetV2 on ImageNet-1K with similar latency and parameters. This work demonstrate that properly designed and optimized vision transformers can achieve high performance even with MobileNet-level size and speed.Comment: Code is available at: https://github.com/snap-research/EfficientForme

Nerfstudio: A Modular Framework for Neural Radiance Field Development

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more. In order to streamline the development and deployment of NeRF research, we propose a modular PyTorch framework, Nerfstudio. Our framework includes plug-and-play components for implementing NeRF-based methods, which make it easy for researchers and practitioners to incorporate NeRF into their projects. Additionally, the modular design enables support for extensive real-time visualization tools, streamlined pipelines for importing captured in-the-wild data, and tools for exporting to video, point cloud and mesh representations. The modularity of Nerfstudio enables the development of Nerfacto, our method that combines components from recent papers to achieve a balance between speed and quality, while also remaining flexible to future modifications. To promote community-driven development, all associated code and data are made publicly available with open-source licensing at https://nerf.studio.Comment: Project page at https://nerf.studi

A wearable freestanding electrochemical sensing system.

To render high-fidelity wearable biomarker data, understanding and engineering the information delivery pathway from epidermally retrieved biofluid to a readout unit are critical. By examining the biomarker information delivery pathway and recognizing near-zero strained regions within a microfluidic device, a strain-isolated pathway to preserve biomarker data fidelity is engineered. Accordingly, a generalizable and disposable freestanding electrochemical sensing system (FESS) is devised, which simultaneously facilitates sensing and out-of-plane signal interconnection with the aid of double-sided adhesion. The FESS serves as a foundation to realize a system-level design strategy, addressing the challenges of wearable biosensing, in the presence of motion, and integration with consumer electronics. To this end, a FESS-enabled smartwatch was developed, featuring sweat sampling, electrochemical sensing, and data display/transmission, all within a self-contained wearable platform. The FESS-enabled smartwatch was used to monitor the sweat metabolite profiles of individuals in sedentary and high-intensity exercise settings