Evaluation codes for MS COCO caption generation.
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 (see Requirements).
- Java 1.8.0
- Python 3
- pycocotools (COCO Python API): https://github.com/cocodataset/cocoapi
To install pycocoevalcap and the pycocotools dependency, run:
pip install git+https://github.com/salaniz/pycocoevalcap
./
- 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
- 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] (http://arxiv.org/pdf/1411.5726.pdf)
- Xinlei Chen (CMU)
- Hao Fang (University of Washington)
- Tsung-Yi Lin (Cornell)
- Ramakrishna Vedantam (Virgina Tech)
- David Chiang (University of Norte Dame)
- Michael Denkowski (CMU)
- Alexander Rush (Harvard University)