pxiangwu / FORB

"FORB: A Flat Object Retrieval Benchmark for Universal Image Embedding", NeurIPS 2023 Datasets and Benchmarks Track

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FORB: A Flat Object Retrieval Benchmark for Universal Image Embedding

Pengxiang Wu, Siman Wang, Kevin Dela Rosa , Derek Hao Hu

NeurIPS 2023 Datasets and Benchmarks Track (Paper Link)


We introduce a benchmark for evaluating the retrieval performance on flat objects. Specifically, we consider the following objects:

  • Animated card
  • Photorealisitc card
  • Book cover
  • Painting
  • Currency
  • Logo
  • Packaged goods
  • Movie poster

In this repository, we provide scripts for downloading the benchmark images, as well as supporting code for evaluating various baselines.

The structure of this repository is:

  • baselines/: contains code for evaluating the performances of different methods.
  • metadata/: the metadata of the benchmark.
  • downloader/: contains code for downloading the benchmark images.
  • metric_helper/: contains some utility functions to help evaluating image retrieval accuracies.

We also offer this Google Drive Link for downloading the benchmark data directly.

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"FORB: A Flat Object Retrieval Benchmark for Universal Image Embedding", NeurIPS 2023 Datasets and Benchmarks Track

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