Benchmark type: SEED-Bench-2 is a comprehensive large-scale benchmark for evaluating Multimodal Large Language Models (MLLMs), featuring 24K multiple-choice questions with precise human annotations. It spans 27 evaluation dimensions, assessing both text and image generation.
Benchmark date: SEED-Bench was collected in November 2023.
Paper or resources for more information: https://github.com/AILab-CVC/SEED-Bench
License: Attribution-NonCommercial 4.0 International. It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use.
Data Sources:
Please contact us if you believe any data infringes upon your rights, and we will remove it.
Where to send questions or comments about the benchmark: https://github.com/AILab-CVC/SEED-Bench/issues
Primary intended uses: SEED-Bench-2 is primarily designed to evaluate Multimodal Large Language Models in text and image generation tasks.
Primary intended users: Researchers and enthusiasts in computer vision, natural language processing, machine learning, and artificial intelligence are the main target users of the benchmark.
14 commits
Benchmark type: SEED-Bench-2 is a comprehensive large-scale benchmark for evaluating Multimodal Large Language Models (MLLMs), featuring 24K multiple-choice questions with precise human annotations. It spans 27 evaluation dimensions, assessing both text and image generation.
Benchmark date: SEED-Bench was collected in November 2023.
Paper or resources for more information: https://github.com/AILab-CVC/SEED-Bench
License: Attribution-NonCommercial 4.0 International. It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use.
Data Sources:
Please contact us if you believe any data infringes upon your rights, and we will remove it.
Where to send questions or comments about the benchmark: https://github.com/AILab-CVC/SEED-Bench/issues
Primary intended uses: SEED-Bench-2 is primarily designed to evaluate Multimodal Large Language Models in text and image generation tasks.
Primary intended users: Researchers and enthusiasts in computer vision, natural language processing, machine learning, and artificial intelligence are the main target users of the benchmark.
14 commits