feiyang-k/curation-bench/clip-benchmark-a-benchmark-suite-for-evaluation-of-vision-language-models-github-clip-benchmark-2023

CLIP-Benchmark: A Benchmark Suite for Evaluation of Vision-Language Models

One-line decision

Use this skill when you need a standardized benchmark suite for evaluating CLIP-style vision-language encoders across dozens of datasets. Avoid it when you only evaluate on ImageNet zero-shot.

Skill metadata

• Skill type: clip-evaluation-suite
• Paper kind: operational-method
• Actionability: high
• Evidence quality: full_paper

Goal

Provide a standardized benchmark suite for evaluating CLIP-style vision-language encoders across dozens of zero-shot classification and retrieval datasets.

Problem signature

• Modality: evaluation suite for CLIP-style encoders.
• Data state: dozens of evaluation datasets for zero-shot and retrieval.
• Scale regime: comprehensive evaluation benchmark.
• Model requirement: Any CLIP-style dual encoder.

Use when

• You train CLIP-style encoders and need comprehensive evaluation.
• You want standardized comparison across models.
• You need more than ImageNet zero-shot evaluation.

Do not use when

• ImageNet-only evaluation is sufficient.
• You are not training CLIP-style models.
• You have custom evaluation needs.

Required inputs

• clip_model: CLIP-style encoder to evaluate.
• benchmark_suite: CLIP-Benchmark evaluation datasets.
• evaluation_code: Standardized evaluation code.

Optional inputs

• custom_datasets: Additional evaluation datasets.

Outputs

• benchmark_results: Comprehensive evaluation across dozens of datasets.
• model_comparison: Comparison with other CLIP models.

Assumptions and prerequisites

• Comprehensive evaluation reveals more than single-dataset testing.
• Standardized evaluation enables fair model comparison.
• Multiple datasets test different capabilities.

Procedure

1. Configure evaluation

Action: Select evaluation datasets and metrics. Why: Flexible configuration for different needs. Note: See paper for details.

1. Run zero-shot classification

Action: Evaluate zero-shot accuracy on classification datasets. Why: Standard CLIP evaluation. Note: See paper for details.

1. Run retrieval evaluation

Action: Evaluate image-text and text-image retrieval. Why: Tests alignment quality. Note: See paper for details.

1. Generate comparison report

Action: Compare results to other CLIP models. Why: Enables standardized comparison. Note: See paper for details.

Parameters to set

• num_datasets — Role: Number of evaluation datasets. How to set: Include all available. Default/range: 40+. Effect: More datasets provide comprehensive evaluation.
• evaluation_metrics — Role: Metrics for evaluation. How to set: Accuracy for classification, R@K for retrieval. Default/range: Standard. Effect: Standard metrics enable comparison.

Validation checks

• Results should be reproducible.
• Comparison should be fair across models.
• Evaluation should cover diverse visual domains.

Failure modes

• Some datasets may not be available.
• Evaluation may be slow for many datasets.
• Not all capabilities are tested.

Adaptation notes for VLM training

• CLIP-Benchmark is the standard evaluation for VLM vision encoders.
• Use to evaluate the impact of data curation on encoder quality.
• Comprehensive evaluation reveals training data effects.

Implementation notes

• Use the CLIP-Benchmark codebase.
• Report all dataset results.
• Track improvements across training iterations.

Evidence from the paper

• CLIP-Benchmark provides standardized evaluation across 40+ datasets.
• Comprehensive evaluation reveals model capabilities beyond ImageNet.
• Standardized comparison enables fair model ranking.
• The benchmark is widely used for CLIP model development.

Source paper

• Title: CLIP-Benchmark: A Benchmark Suite for Evaluation of Vision-Language Models
• Year: 2023
• Venue: GitHub
• Paper ID: github-clip-benchmark-2023
• URL: https://github.com/LAION-AI/CLIP_benchmark
• arXiv ID: N/A