{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Evaluates image captions using multiple automatic metrics (BLEU, METEOR, ROUGE-L, CIDEr, SPICE) designed for the MS COCO dataset, providing Python 3 support for caption generation assessment.","skillfed_tags":["caption-evaluation","coco-dataset","nlp-metrics"],"use_cases":["Benchmark image captioning models against MS COCO dataset using standardized evaluation metrics.","Compare multiple caption generation systems with consistent, reproducible scores.","Validate caption quality during model development and hyperparameter tuning.","Generate evaluation reports for research papers using established COCO evaluation protocols.","Integrate caption evaluation into automated testing pipelines for vision-language models."],"what_it_does":"Pycocoevalcap is a Python 3 port of the original MS COCO caption evaluation toolkit, providing five automatic metrics for assessing the quality of machine-generated image captions. It wraps evaluation algorithms (BLEU, METEOR, ROUGE-L, CIDEr, and SPICE) that compare generated captions to reference captions, producing scores that measure different aspects of caption quality\u2014fluency, semantic similarity, and semantic propositional content.\n\nThe package depends on pycocotools for the COCO API and requires Java 1.8.0 for some components. SPICE evaluation automatically downloads Stanford CoreNLP on first use and caches parsed sentences to speed up repeated evaluations. The package is stable but dormant; it has not been updated since its initial release in November 2020, though the repository remains accessible and the underlying evaluation metrics are well-established in the computer vision and NLP research communities.","worth_installing":"Yes, if you are evaluating captions on the MS COCO dataset or need the specific metrics this package provides. The low install friction, stable API, and absence of known vulnerabilities make it reliable for established evaluation workflows. However, verify the license terms first, and be aware that the package is dormant\u2014no updates are expected, and you should confirm that all five metrics work correctly in your environment, particularly SPICE's Java and CoreNLP dependencies."},"id":"pycocoevalcap","links":{"html":"https://skillfed.io/packages/pycocoevalcap","md":"https://skillfed.io/packages/pycocoevalcap.md","pypi":"https://pypi.org/project/pycocoevalcap/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2020-11-18","license_spdx":null,"license_treatment":"unclear","name":"pycocoevalcap","python_support":"supports_current","summary":"MS-COCO Caption Evaluation for Python 3"},"popularity":{"monthly_downloads":1013087,"position":4509,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.2"}