Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
Open accessAbstract
The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-toapples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [30], R-FCN [6] and SSD [25] systems, which we view as meta-architectures and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.
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Two-stage detectors tend to be more flexible and more accurate, while one-stage detectors are often considered simpler and more efficient by leveraging predefined anchors.
“While two-stage detectors tend to be more flexible and more accurate, one-stage detectors are often considered to be simpler and more efficient by leveraging predefined anchors [17].”
From EfficientDet: Scalable and Efficient Object Detection (2019) · cites this paper as [17]
Huang et al. found a new combination of factors (number of proposals, input image size, and feature extractor) that outperforms the state-of-the-art method for object recognition.
“For the object recognition application, Huang et al. [120] investigate the influence of the main factors, e.g., number of proposals, input image size, and the selection of feature extractor, on inference speed and accuracy.Based on their experiment results, a new combination of these factors is found to outperform the state-of-the-art method.”
From Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing (2019) · cites this paper as [120]
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“Despite various advances, high computational costs and complexity associated with HE still persist when integrating in PPML [2].”
- Authors
- Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., & Murphy, K.
- Affiliations
- Siberian Branch of the Russian Academy of Sciences (RU)
- ORCID
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 3296-3297
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2557728737
- Semantic Scholar
- a312a573ef81793d56401e932ef6c9498791a3d1
- Cited by
- 2,743
- References
- 42
- Citations by year
- 2026: 69 · 2025: 138 · 2024: 159 · 2023: 216 · 2022: 301 · 2021: 452 · 2020: 465 · 2019: 528 · 2018: 346 · 2017: 68 · 2016: 1
- FWCI
- 112.49
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- speed-accuracy trade-off, Faster R-CNN, R-FCN, SSD, feature extractor, memory usage, real-time detection, mobile deployment, COCO benchmark, meta-architecture, image resolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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