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Speed/accuracy trade-offs for modern convolutional object detectors (1611.10012v3)

Published 30 Nov 2016 in cs.CV

Abstract: 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-to-apples 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 [Ren et al., 2015], R-FCN [Dai et al., 2016] and SSD [Liu et al., 2015] 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.

Citations (2,522)

Summary

  • The paper presents a unified TensorFlow implementation of Faster R-CNN, R-FCN, and SSD to enable systematic comparisons.
  • It evaluates how varying image resolution, feature extractors, and region proposals affect detection accuracy and processing speed.
  • The study identifies key sweet spots in the trade-off curves, offering actionable insights for deployment in resource-constrained environments.

Speed/Accuracy Trade-offs for Modern Convolutional Object Detectors

The paper "Speed/Accuracy Trade-offs for Modern Convolutional Object Detectors" by Jonathan Huang et al. offers a comprehensive investigation into the balance of speed, memory, and accuracy in modern convolutional object detection architectures. The objective is to provide practitioners with practical insights into selecting the optimal detection architecture tailored to their specific application and platform constraints.

Methodology

The paper primarily assesses three widely-adopted meta-architectures: Faster R-CNN, R-FCN, and SSD, implemented within the TensorFlow framework. These meta-architectures were evaluated using various feature extractors, including VGG, Resnet-101, Inception V2, Inception V3, Inception-Resnet V2, and MobileNet. The paper systematically explores the impact of several critical parameters, such as input image resolution and the number of proposals, on the speed/accuracy trade-off.

Key Contributions

  1. Unified Implementation: The authors offer a flexible and unified implementation of Faster R-CNN, R-FCN, and SSD meta-architectures in TensorFlow. This approach allows for an exhaustive and consistent comparison across different configurations.
  2. Extensive Experiments: By varying meta-architecture, feature extractor, image resolution, and other parameters, the paper traces out the speed/accuracy trade-off curves. This facilitates an apples-to-apples comparison that is often missing in existing literature.
  3. Sweet Spots in Trade-offs: The paper identifies "sweet spots" on the accuracy/speed trade-off curves where improvements in accuracy are only possible by significant sacrifices in speed. For example, using fewer region proposals can significantly speed up Faster R-CNN with minimal accuracy loss, making it competitive with SSD and R-FCN.
  4. Novel Combinations and State-of-the-Art Results: Several meta-architecture and feature extractor combinations reported are novel and have not appeared before in the literature. These combinations contributed to training the winning entry of the 2016 COCO object detection challenge.

Numerical Results

  • The fastest models utilized the SSD meta-architecture with Inception V2 and MobileNet feature extractors. SSD with MobileNet, running in real-time, achieved an mAP of 19.3.
  • Sweet spot models included Faster R-CNN with Resnet 101 limited to 100 proposals and R-FCN with Resnet 101 using 300 proposals, balancing speed and accuracy with mAPs of 32 and 30.4, respectively.
  • The most accurate model was Faster R-CNN with Inception Resnet V2 at stride 8, achieving a state-of-the-art mAP of 35.7.

Implications and Future Directions

From a practical standpoint, this paper highlights essential considerations for deploying object detection models in various resource-constrained environments such as mobile devices or real-time systems. The insights into the effects of input resolution and a reduced number of region proposals offer pathways to optimize existing architectures without substantial trades in accuracy.

Theoretically, this research underscores the significance of unified benchmarking frameworks for objective model comparisons. Future work could extend these methodologies to cover more recent architectures and additional meta-architecture variants, making the results more comprehensive.

Conclusion

This paper stands as a significant resource for researchers and practitioners in computer vision, presenting well-structured insights into the often-complicated balance between detection speed, memory usage, and accuracy. The unified, systematic approach adopted here serves not just to benchmark models but also to illustrate the nuanced interplay between various architectural choices, thereby aiding informed decision-making for real-world applications. As AI continues to evolve, such thorough comparative studies will be invaluable in guiding the development and deployment of efficient and effective object detection systems.

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