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National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics

NCVPRIPG 2019: Computer Vision, Pattern Recognition, Image Processing, and Graphics pp 450–459Cite as

Semantic Segmentation Datasets for Resource Constrained Training

Part of the Communications in Computer and Information Science book series (CCIS,volume 1249)

Abstract

Several large scale datasets, coupled with advances in deep neural network architectures have been greatly successful in pushing the boundaries of performance in semantic segmentation in recent years. However, the scale and magnitude of such datasets prohibits ubiquitous use and widespread adoption of such models, especially in settings with serious hardware and software resource constraints. Through this work, we propose two simple variants of the recently proposed IDD dataset, namely IDD-mini and IDD-lite, for scene understanding in unstructured environments. Our main objective is to enable research and benchmarking in training segmentation models. We believe that this will enable quick prototyping useful in applications like optimum parameter and architecture search, and encourage deployment on low resource hardware such as Raspberry Pi. We show qualitatively and quantitatively that with only 1 h of training on 4 GB GPU memory, we can achieve satisfactory semantic segmentation performance on the proposed datasets.

Keywords

  • Semantic segmentation
  • Neural architecture search

A. Mishra, S. Kumar and T. Kalluri—Equal contribution.

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Notes

  1. 1.

    https://idd.insaan.iiit.ac.in/.

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Authors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    Ashutosh Mishra, Sudhir Kumar, Tarun Kalluri, Girish Varma & C. V. Jawahar

  2. University at Buffalo, State University of New York, Buffalo, USA

    Sudhir Kumar

  3. University of California, San Diego, USA

    Tarun Kalluri & Manmohan Chandraker

  4. Intel Bangalore, Bangalore, USA

    Anbumani Subramaian

Authors
  1. Ashutosh Mishra
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  2. Sudhir Kumar
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  3. Tarun Kalluri
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  4. Girish Varma
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  5. Anbumani Subramaian
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  6. Manmohan Chandraker
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  7. C. V. Jawahar
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Corresponding author

Correspondence to Ashutosh Mishra .

Editor information

Editors and Affiliations

  1. Department of Computational and Data Sciences, Indian Institute of Science Bangalore, Bangalore, India

    R. Venkatesh Babu

  2. Department of Electrical Engineering, Indian Institute of Technology Dharwad, Dharwad, India

    Mahadeva Prasanna

  3. Computer Science & Engineering, Indian Institute of Technology Kanpur, Kanpur, India

    Vinay P. Namboodiri

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Mishra, A. et al. (2020). Semantic Segmentation Datasets for Resource Constrained Training. In: Babu, R.V., Prasanna, M., Namboodiri, V.P. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2019. Communications in Computer and Information Science, vol 1249. Springer, Singapore. https://doi.org/10.1007/978-981-15-8697-2_42

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  • DOI: https://doi.org/10.1007/978-981-15-8697-2_42

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