We use the latest version (1.5) of the Code. Meteor: Project page with related publications.BLEU: BLEU: a Method for Automatic Evaluation of Machine Translation.PTBTokenizer: We use the Stanford Tokenizer which is included in Stanford CoreNLP 3.4.1.Microsoft COCO Captions: Data Collection and Evaluation Server.In the same file, caching can be turned off by removing the '-cache' argument to 'spice_cmd'. The cache directory can be moved by setting 'CACHE_DIR' in. This dramatically speeds up repeated evaluations. Note: SPICE will try to create a cache of parsed sentences in.This will be done automatically the first time the SPICE evaluation is performed. SPICE requires the download of Stanford CoreNLP 3.6.0 code and models.

tokenizer: Python wrapper of Stanford CoreNLP PTBTokenizer.

eval.py: The file includes COCOEavlCap class that can be used to evaluate results on COCO.

See the example script: example/coco_eval_example.py Files To install pycocoevalcap and the pycocotools dependency ( ), run: pip install pycocoevalcap Ĭaption evaluation depends on the COCO API that natively supports Python 3. The code is derived from the original repository that supports Python 2.7. This repository provides Python 3 support for the caption evaluation metrics used for the MS COCO dataset.

Evaluation codes for MS COCO caption generation.

Instaling Coco