Evaluation codes for MS COCO caption generation.
- java 1.8.0
- python 2.7
./
- cocoEvalCapDemo.py (demo script)
./annotation
- captions_val2014.json (COCO 2014 caption validation set)
- More detials can be found under download tab on COCO dataset
./results
- captions_val2014_fakecap_results.json (example fake results for running demo)
- More details can be found under evaluate->format tab on COCO dataset
./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
- 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)