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Microsoft COCO Caption Evaluation

Evaluation codes for MS COCO caption generation.

Description

This repository provides Python 3 support for the caption evaluation metrics used for the MS COCO dataset.

The code is derived from the original repository that supports Python 2.7: https://github.com/tylin/coco-caption.
Caption evaluation depends on the COCO API that natively supports Python 3.

Requirements

• Java 1.8.0
• Python 3
• For CLIPScore, both pytorch and OpenAI's CLIP are required.

Installation

To install pycocoevalcap and the pycocotools dependency (https://github.com/cocodataset/cocoapi), run:

pip install git+https://github.com/jmhessel/pycocoevalcap

Usage

See the example script: example/coco_eval_example.py

Files

./

• eval.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
• clipscore: CLIPScore evaluation codes

Setup

• SPICE requires the download of Stanford CoreNLP 3.6.0 code and models. This will be done automatically the first time the SPICE evaluation is performed.
• Note: SPICE will try to create a cache of parsed sentences in ./spice/cache/. This dramatically speeds up repeated evaluations. The cache directory can be moved by setting 'CACHE_DIR' in ./spice. In the same file, caching can be turned off by removing the '-cache' argument to 'spice_cmd'.

References

• Microsoft COCO Captions: Data Collection and Evaluation Server
• PTBTokenizer: We use the Stanford Tokenizer which is included in Stanford CoreNLP 3.4.1.
• BLEU: BLEU: a Method for Automatic Evaluation of Machine Translation
• Meteor: Project page with related publications. We use the latest version (1.5) of the Code. Changes have been made to the source code to properly aggreate the statistics for the entire corpus.
• Rouge-L: ROUGE: A Package for Automatic Evaluation of Summaries
• CIDEr: CIDEr: Consensus-based Image Description Evaluation
• SPICE: SPICE: Semantic Propositional Image Caption Evaluation
• CLIPScore: CLIPScore: A Reference-free Evaluation Metric for Image Captioning

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)
• Jungo Kasai (UW): for helping to squash a bug with the CLIPScore implementation