LMMs-Eval

LMMs-Eval is an evaluation toolkit for multimodal AI models across text, image, video, and audio tasks.

LMMs-Eval is a toolkit designed for evaluating multimodal artificial intelligence models across diverse tasks involving text, images, video, and audio. Developed as part of LMMs-Lab's open research initiatives, the software supports researchers and developers working on multimodal intelligence, benchmarks, and model systems. The platform shares tooling and frameworks discovered through ongoing research into multimodal models. Users can access evaluations and benchmarks to test model performance. It serves the AI research community by offering shared resources for perceptual intelligence and multimodal reasoning.

LMMs-Eval at a glance

Best for: Researcher, Software engineer
Runs on: open source
Pricing: Free
Released: March 2024
Category: AI research tool
Website: lmms-lab.com

Key features

Multimodal model evaluation
Support for text, image, video, and audio tasks
Benchmark tooling
Open research integration
Perceptual intelligence testing

Use it for

Evaluating multimodal AI models
Running benchmark tests on AI systems
Conducting open multimodal AI research

Alternatives to LMMs-Eval

VLMEvalKit — Evaluate large vision-language models across numerous benchmarks with a single command.
EvalScope — It combines model capability benchmarks with inference performance testing in one tool.
Lighteval — Evaluates LLMs across multiple backends with thousands of built-in tasks.
OpenCompass — It offers an objective, comprehensive, and credible open-source AI evaluation system.
OpenVLHarness — Lifts six multimodal model backbones by 9.7 to 12.6 points across 22 benchmarks.
MTEB — Evaluates embedding models across over 1000 tasks and 1000 languages.
Vlmhub — Run multimodal prompts across multiple local and cloud backends without rewriting inference code.
Evals Skills — It helps avoid common mistakes and pitfalls when building AI evaluation suites.
Vegaml — Runs locally on your hardware with calibrated probabilities and conformal answer sets.
LLM Wiki Research — Compiles parallel agent research into source-backed markdown knowledge bases.
Rsi Eval — It supplies the evaluation boundary so methods can use existing benchmarks without reimplementing execution.
Homi — It answers complex audio questions natively with high accuracy.

All LMMs-Eval alternatives