pycocoevalcap
MS-COCO Caption Evaluation for Python 3
Decision gist · record as of 2026-08-14
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—no updates are expected, and you should confirm that all five metrics work correctly in your environment, particularly SPICE's Java and CoreNLP dependencies.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Java 1.8.0 is required; SPICE metric will automatically download Stanford CoreNLP 3.6.0 on first use.
- Low install friction with a single runtime dependency (pycocotools).
- The package is dormant—last release was 2020-11-18 and no updates since, though the repository remains active with recent commits (2024-08-01).
License · maintenance · safety
(unclear) — License status is unclear; no SPDX identifier or raw license text is provided. Verify the actual license terms in the repository before using in proprietary or restricted contexts.
last release 2020-11-18 (2095 days) · last repo commit 2024-08-01 · 344 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,013,087 downloads/mo, #4,509 on PyPI
Alternatives
Verify before relying
pip install pycocoevalcap
from pycocoevalcap.eval import COCOEvalCap
coco_eval = COCOEvalCap(coco, coco_result)
coco_eval.evaluate()- Whether the package's license is compatible with your intended use (license treatment is unclear).
- Current state of SPICE's Stanford CoreNLP download and cache behavior in modern environments.
- Whether all five metrics (BLEU, METEOR, ROUGE-L, CIDEr, SPICE) are equally maintained and reliable.
What it is and 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—fluency, semantic similarity, and semantic propositional content.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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—no updates are expected, and you should confirm that all five metrics work correctly in your environment, particularly SPICE's Java and CoreNLP dependencies.
Install
pycocoevalcap on PyPI
Before you install
Low install friction with a single runtime dependency (pycocotools). The package is dormant—last release was 2020-11-18 and no updates since, though the repository remains active with recent commits (2024-08-01). Suitable for stable evaluation workflows but not for ongoing feature development.
Java 1.8.0 is required; SPICE metric will automatically download Stanford CoreNLP 3.6.0 on first use.
License in practice
License status is unclear; no SPDX identifier or raw license text is provided. Verify the actual license terms in the repository before using in proprietary or restricted contexts.
Quickstart
pip install pycocoevalcap
from pycocoevalcap.eval import COCOEvalCap
coco_eval = COCOEvalCap(coco, coco_result)
coco_eval.evaluate()Verify before relying
- Whether the package's license is compatible with your intended use (license treatment is unclear).
- Current state of SPICE's Stanford CoreNLP download and cache behavior in modern environments.
- Whether all five metrics (BLEU, METEOR, ROUGE-L, CIDEr, SPICE) are equally maintained and reliable.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepycocotools |
| Maintenance | Dormant 2,095 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,013,087 / month, #4,509 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: pycocoevalcap-1.2-py3-none-any.whl
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