clip-benchmark
CLIP-like models benchmarks on various datasets
Decision gist · record as of 2026-08-14
Yes, if you need to systematically evaluate CLIP-like models on standard benchmarks. The low install friction and permissive license make it accessible. However, the aging maintenance status (385 days since release) and Pre-Alpha development status mean you should verify compatibility with your PyTorch and transformers versions before relying on it for production comparisons. No known security vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch, torchvision, and transformers; evaluation tasks require sufficient GPU memory depending on model size and batch size.
- Low install friction with a pure-Python wheel.
- Maintenance status is aging (385 days since last release), though the package remains functional and receives occasional updates.
License · maintenance · safety
MIT license (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions.
last release 2025-07-25 (385 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 449,044 downloads/mo, #6,598 on PyPI
Alternatives
Verify before relying
pip install clip-benchmark
clip_benchmark eval --dataset=cifar10 --task=zeroshot_classification \
--pretrained=laion400m_e32 --model=ViT-B-32-quickgelu \
--output=result.json --batch_size=64- Whether aging maintenance status (385 days since release) affects compatibility with recent PyTorch or transformers versions.
- Support status for Python versions beyond 3.8 (classifiers list only up to 3.8).
What it is and what it does
CLIP Benchmark is a standardized evaluation framework for CLIP-like vision-language models. It provides a command-line interface to benchmark models like OpenCLIP, Japanese CLIP, and NLLB CLIP across multiple datasets and tasks. The package handles zero-shot image classification and retrieval, linear probing, and image captioning, supporting datasets from torchvision, TensorFlow Datasets, and VTAB, as well as multilingual and compositional tasks.
You specify a model, dataset, and task; the framework loads the model, runs inference, and outputs evaluation metrics to JSON. Results can be aggregated into CSV tables for comparison. The package depends on torch, torchvision, transformers, open-clip-torch, scikit-learn, pycocoevalcap, webdataset, and tqdm—a substantial set of deep-learning dependencies that must be installed alongside it.
Use it for
- Compare zero-shot classification accuracy of different CLIP variants on standard datasets like CIFAR-10 or ImageNet.
- Evaluate multilingual CLIP models (Japanese CLIP, NLLB) on language-specific or cross-lingual retrieval tasks.
- Run linear probing experiments to measure how well frozen CLIP embeddings transfer to downstream tasks.
- Benchmark image captioning models on COCO captions with standard metrics via pycocoevalcap.
- Generate reproducible evaluation tables across multiple models and datasets for research papers or model selection.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to systematically evaluate CLIP-like models on standard benchmarks.
The low install friction and permissive license make it accessible. However, the aging maintenance status (385 days since release) and Pre-Alpha development status mean you should verify compatibility with your PyTorch and transformers versions before relying on it for production comparisons. No known security vulnerabilities.
Install
clip-benchmark on PyPI
Before you install
Low install friction with a pure-Python wheel. Maintenance status is aging (385 days since last release), though the package remains functional and receives occasional updates.
Requires torch, torchvision, and transformers; evaluation tasks require sufficient GPU memory depending on model size and batch size.
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions.
Quickstart
pip install clip-benchmark
clip_benchmark eval --dataset=cifar10 --task=zeroshot_classification \
--pretrained=laion400m_e32 --model=ViT-B-32-quickgelu \
--output=result.json --batch_size=64Verify before relying
- Whether aging maintenance status (385 days since release) affects compatibility with recent PyTorch or transformers versions.
- Support status for Python versions beyond 3.8 (classifiers list only up to 3.8).
Package facts
| License | MIT license permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagestorchtorchvisiontqdmscikit-learnopen-clip-torchpycocoevalcapwebdatasettransformers |
| Maintenance | Aging 385 days since the last release |
| First released | |
| Downloads | 449,044 / month, #6,598 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 2 - Pre-AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8 |
Evidence: clip_benchmark-1.6.2-py2.py3-none-any.whl
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See also open-clip-torch · rf100vl · clip-anytorch · clip-interrogator · ogb · pycocoevalcap · faster-coco-eval · unitxt · lpips · lm-eval