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README.md

Microsoft COCO Caption Evaluation

Evaluation codes for MS COCO caption generation.

Requirements

  • java 1.8.0
  • python 2.7

Files

./

  • cocoEvalCapDemo.py (demo script)

./annotation

  • captions_val2014.json (MS COCO 2014 caption validation set)
  • Visit MS COCO download page for more details.

./results

  • captions_val2014_fakecap_results.json (an example of fake results for running demo)
  • Visit MS COCO format page for more details.

./pycocoevalcap: The folder where all evaluation codes are stored.

  • evals.py: The file includes COCOEavlCap class that can be used to evaluate results on COCO.
  • tokenizer: Python wrapper of Stanford CoreNLP PTBTokenizer
  • bleu: Bleu evalutation codes
  • meteor: Meteor evaluation codes
  • rouge: Rouge-L evaluation codes
  • cider: CIDEr evaluation codes
  • spice: SPICE evaluation codes

Setup

  • You will first need to download the Stanford CoreNLP 3.6.0 code and models for use by SPICE. To do this, run: ./get_stanford_models.sh
  • Note: SPICE will try to create a cache of parsed sentences in ./pycocoevalcap/spice/cache/. This dramatically speeds up repeated evaluations. The cache directory can be moved by setting 'CACHE_DIR' in ./pycocoevalcap/spice. In the same file, caching can be turned off by removing the '-cache' argument to 'spice_cmd'.

References

Developers

  • Xinlei Chen (CMU)
  • Hao Fang (University of Washington)
  • Tsung-Yi Lin (Cornell)
  • Ramakrishna Vedantam (Virgina Tech)

Acknowledgement

  • David Chiang (University of Norte Dame)
  • Michael Denkowski (CMU)
  • Alexander Rush (Harvard University)