Deep Visual Geo-localization Benchmark

• URL: http://arxiv.org/abs/2204.03444v2
• Date: Fri, 9 Jun 2023 10:18:20 GMT
• Title: Deep Visual Geo-localization Benchmark
• Abstract summary: We propose a new open-source benchmarking framework for Visual Geo-localization (VG) This framework allows to build, train, and test a wide range of commonly used architectures. Code and trained models are available at https://deep-vg-bench.herokuapp.com/.
• Score: 42.675402470265674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architectures, with the flexibility to change individual components of a geo-localization pipeline. The purpose of this framework is twofold: i) gaining insights into how different components and design choices in a VG pipeline impact the final results, both in terms of performance (recall@N metric) and system requirements (such as execution time and memory consumption); ii) establish a systematic evaluation protocol for comparing different methods. Using the proposed framework, we perform a large suite of experiments which provide criteria for choosing backbone, aggregation and negative mining depending on the use-case and requirements. We also assess the impact of engineering techniques like pre/post-processing, data augmentation and image resizing, showing that better performance can be obtained through somewhat simple procedures: for example, downscaling the images' resolution to 80% can lead to similar results with a 36% savings in extraction time and dataset storage requirement. Code and trained models are available at https://deep-vg-bench.herokuapp.com/.

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