MMBench

MMBench is a multimodal benchmarkTesting Framework, aims to provide a comprehensive evaluation system for measuring and understanding the performance of multimodal models in different scenarios.

Background and purpose

With the rapid development of large-scale visual language models, they have demonstrated powerful perception and reasoning capabilities for visual information. However, how to effectively evaluate the performance of these models remains a challenge that hinders the development of future models.MMBench was created to address this problem by providing an objective benchmark for system design to robustly evaluate the various capabilities of visual language models.

Main features

1. Integrated assessment process: MMBench has developed an evaluation process with a step-by-step breakdown from perceptual to cognitive capabilities, covering 20 fine-grained capabilities. These capability dimensions cover a wide range of aspects such as target detection, text recognition, action recognition, image understanding, etc., thus enabling a comprehensive assessment of the performance of multimodal models.
2. Carefully labeled datasets: MMBench uses a large number of carefully labeled datasets that exceed similar existing benchmarks in terms of the number and variety of questions and competencies assessed. This ensures the accuracy and reliability of the assessments.
3. CircularEval strategy: MMBench introduces a new CircularEval strategy, which evaluates the performance of a model by cyclically disrupting the options and verifying the consistency of the output. CircularEval is more robust and reliable than traditional rule-matching based evaluation methods.
4. ChatGPT-based matching model: MMBench also uses a ChatGPT-based matching model to output matches to options. Even if the model does not output as instructed, it can accurately match to the most reasonable option, thus improving the accuracy of the evaluation.

Evaluation process

MMBench's assessment process consists of the following main steps:

1. Issue Selection: Selection of assessment questions from carefully labeled datasets.
2. Cluttering of options: Cyclic disruption of the question's options to eliminate the effect of the order of the options on the results of the assessment.
3. model prediction: Let the multimodal model predict the disrupted options.
4. Validation of results: Verify the consistency of the model predictions and evaluate the performance of the model according to the CircularEval strategy.

Applications and impacts

MMBench, as an open source project, has attracted the attention of many researchers and developers. It provides an open platform that encourages the community to contribute and integrate new multimodal models and tasks. With MMBench, users can easily compare existing multimodal models or use it as a starting point for new model development. In addition, the evaluation results of MMBench can provide valuable references for model optimization and improvement.

Project Addresses and Documentation

MMBench open source project address is :https://gitcode.com/gh_mirrors/mm/MMBench. Users can find resources such as the project's source code, documentation, and tutorials on how to use it at this address. By consulting the official documentation, users can gain a deeper understanding of how to use MMBench and its advanced features.

MMBench is a powerful and easy-to-use multimodal benchmarking framework. It provides a comprehensive evaluation system for measuring and understanding the performance of multimodal models in different scenarios. Through MMBench's evaluation, users can better understand the strengths and weaknesses of the models and provide valuable references for model optimization and improvement.