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Made Python 3 compatible and included Word Mover's Distance metric. #37

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Made Python 3 compatible and included Word Mover's Distance metric. #37

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15 changes: 13 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
@@ -5,7 +5,8 @@ Evaluation codes for MS COCO caption generation.

## Requirements ##
- java 1.8.0
- python 2.7
- python 3
- gensim

## Files ##
./
@@ -27,12 +28,15 @@ Evaluation codes for MS COCO caption generation.
- rouge: Rouge-L evaluation codes
- cider: CIDEr evaluation codes
- spice: SPICE evaluation codes
- wmd: Word Mover's Distance evaluation codes

## Setup ##

- You will first need to download the [Stanford CoreNLP 3.6.0](http://stanfordnlp.github.io/CoreNLP/index.html) code and models for use by SPICE. To do this, run:
./get_stanford_models.sh
bash 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'.
- You will also need to download the Google News negative 300 word2vec model for use by WMD. To do this, run:
bash get_google_word2vec_model.sh

## References ##

@@ -43,6 +47,12 @@ Evaluation codes for MS COCO caption generation.
- Rouge-L: [ROUGE: A Package for Automatic Evaluation of Summaries](http://anthology.aclweb.org/W/W04/W04-1013.pdf)
- CIDEr: [CIDEr: Consensus-based Image Description Evaluation](http://arxiv.org/pdf/1411.5726.pdf)
- SPICE: [SPICE: Semantic Propositional Image Caption Evaluation](https://arxiv.org/abs/1607.08822)
- WMD: [From word embeddings to document distances](http://proceedings.mlr.press/v37/kusnerb15.html) (original metric publication) and [Re-evaluating Automatic Metrics for Image Captioning](http://aclweb.org/anthology/E17-1019) (publication with metric adapted for caption generation)

Also,

- Stop words distributed by the NLTK Stopwords Corpus [nltk.corpus.stopwords.words('english')], which originate from [https://anoncvs.postgresql.org/cvsweb.cgi/pgsql/src/backend/snowball/stopwords/] and later augmented at [https://github.com/nltk/nltk_data/issues/22], were extracted and put in a text file in pycocoevalcap/wmd/data to avoid requiring users to install NLTK.
- Special thanks to David Semedo [https://github.com/davidfsemedo/coco-caption] for writing a Python 3 compatible version of coco-caption first and which was used as a reference to help make this fork.

## Developers ##
- Xinlei Chen (CMU)
@@ -54,3 +64,4 @@ Evaluation codes for MS COCO caption generation.
- David Chiang (University of Norte Dame)
- Michael Denkowski (CMU)
- Alexander Rush (Harvard University)
- Mert Kilickaya (Hacettepe University)
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