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"name": "MMBench",
"release_date": "2024-01-26",
"subsets": {
"cc": {
"language": [
"zh"
],
"modalities": [
"single_image_start"
],
"task_type": "multiple_choice_qa",
"score_pipeline": [
"rule-match",
"llm-match"
],
"score_protocol": {
"reference": "vlmevalkit@vlmeval/dataset/image_mcq.py:269-274 — listinstr(['mmbench','ccbench',...]) forces circular=True -> vlmevalkit@vlmeval/dataset/utils/multiple_choice.py:511-582 mcq_circular_eval: rotation group (index % 1e6) scores hit=1 only if EVERY rotation is answered correctly; per-rotation grading = can_infer prefetch + GPT extractor (vlmeval/dataset/utils/multiple_choice.py:359-407)",
"note": "Official protocol is CircularEval: a question counts correct only if all option rotations are correct. The mm-eval copy ships pre-rotated rows (VLMEvalKit index convention: rotation ids = base_id + k*1e6, verified via HF rows, e.g. mmbench_en_dev_1001484 / mmbench_cc_test_1000524); a per-sample scorer reports vanilla per-rotation accuracy, systematically higher than official CircularEval. Circular group id is derivable as int(id_suffix) % 1e6. cc subset is CCBench (zh cultural), also circular per the same listinstr branch; test-split answers ARE present (verified 'A' etc.)."
},
"prompt_template": "<image>{% if hint %}Hint: {{ hint }}\n{% endif %}Question: {{ question }}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}\n{% endfor %}Please select the correct answer from the options above. ",
"mapping_from_source": {
"media": {
"from": "image",
"type": "list",
"min_items": 1,
"max_items": 1
},
"id": {
"from": "id"
},
"question": {
"from": "question"
},
"answer": {
"from": "answer",
"optional": true
},
"hint": {
"from": "hint",
"optional": true
},
"options": {
"from": "options",
"optional": true,
"note": "list source values are normalized to {A,B,...} dict"
},
"extra": {
"source_id": {
"from": "source_id"
},
"category": {
"from": "category"
},
"L2_category": {
"from": "L2_category"
},
"source": {
"from": "source"
}
},
"source": {
"format": "huggingface",
"url": {
"cc_test": "https://huggingface.co/datasets/lmms-lab/MMBench"
}
}
},
"prompt_template_source": {
"origin": "official",
"reference": "https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/image_mcq.py#L212-L247 (ImageMCQDataset.build_prompt)",
"notes": "Tier 3: VLMEvalKit canonical MCQ template (byte-for-byte)"
}
},
"cn": {
"language": [
"zh"
],
"modalities": [
"single_image_start"
],
"task_type": "multiple_choice_qa",
"score_pipeline": [
"rule-match",
"llm-match"
],
"score_protocol": {
"reference": "vlmevalkit@vlmeval/dataset/image_mcq.py:269-274 — listinstr(['mmbench','ccbench',...]) forces circular=True -> vlmevalkit@vlmeval/dataset/utils/multiple_choice.py:511-582 mcq_circular_eval: rotation group (index % 1e6) scores hit=1 only if EVERY rotation is answered correctly; per-rotation grading = can_infer prefetch + GPT extractor (vlmeval/dataset/utils/multiple_choice.py:359-407)",
"note": "Official protocol is CircularEval: a question counts correct only if all option rotations are correct. The mm-eval copy ships pre-rotated rows (VLMEvalKit index convention: rotation ids = base_id + k*1e6, verified via HF rows, e.g. mmbench_en_dev_1001484 / mmbench_cc_test_1000524); a per-sample scorer reports vanilla per-rotation accuracy, systematically higher than official CircularEval. Circular group id is derivable as int(id_suffix) % 1e6. cn test split ships empty answers (official answers withheld, submission-only) — only dev is locally scorable (verified via HF rows)."
},
"prompt_template": "<image>{% if hint %}Hint: {{ hint }}\n{% endif %}Question: {{ question }}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}\n{% endfor %}Please select the correct answer from the options above. ",
"mapping_from_source": {
"media": {
"from": "image",
"type": "list",
"min_items": 1,
"max_items": 1
},
"id": {
"from": "id"
},
"question": {
"from": "question"
},
"answer": {
"from": "answer",
"optional": true
},
"hint": {
"from": "hint",
"optional": true
},
"options": {
"from": "options",
"optional": true,
"note": "list source values are normalized to {A,B,...} dict"
},
"extra": {
"source_id": {
"from": "source_id"
},
"category": {
"from": "category"
},
"L2_category": {
"from": "L2_category"
},
"source": {
"from": "source"
}
},
"source": {
"format": "huggingface",
"url": {
"cn_dev": "https://huggingface.co/datasets/lmms-lab/MMBench",
"cn_test": "https://huggingface.co/datasets/lmms-lab/MMBench"
}
}
},
"prompt_template_source": {
"origin": "official",
"reference": "https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/image_mcq.py#L212-L247 (ImageMCQDataset.build_prompt)",
"notes": "Tier 3: VLMEvalKit canonical MCQ template (byte-for-byte); English wrapper preserved for Chinese subset per VLMEvalKit convention"
}
},
"en": {
"language": [
"en"
],
"modalities": [
"single_image_start"
],
"task_type": "multiple_choice_qa",
"score_pipeline": [
"rule-match",
"llm-match"
],
"score_protocol": {
"reference": "vlmevalkit@vlmeval/dataset/image_mcq.py:269-274 — listinstr(['mmbench','ccbench',...]) forces circular=True -> vlmevalkit@vlmeval/dataset/utils/multiple_choice.py:511-582 mcq_circular_eval: rotation group (index % 1e6) scores hit=1 only if EVERY rotation is answered correctly; per-rotation grading = can_infer prefetch + GPT extractor (vlmeval/dataset/utils/multiple_choice.py:359-407)",
"note": "Official protocol is CircularEval: a question counts correct only if all option rotations are correct. The mm-eval copy ships pre-rotated rows (VLMEvalKit index convention: rotation ids = base_id + k*1e6, verified via HF rows, e.g. mmbench_en_dev_1001484 / mmbench_cc_test_1000524); a per-sample scorer reports vanilla per-rotation accuracy, systematically higher than official CircularEval. Circular group id is derivable as int(id_suffix) % 1e6. en test split ships empty answers (official answers withheld, submission-only) — only dev is locally scorable (verified via HF rows)."
},
"prompt_template": "<image>{% if hint %}Hint: {{ hint }}\n{% endif %}Question: {{ question }}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}\n{% endfor %}Please select the correct answer from the options above. ",
"mapping_from_source": {
"media": {
"from": "image",
"type": "list",
"min_items": 1,
"max_items": 1
},
"id": {
"from": "id"
},
"question": {
"from": "question"
},
"answer": {
"from": "answer",
"optional": true
},
"hint": {
"from": "hint",
"optional": true
},
"options": {
"from": "options",
"optional": true,
"note": "list source values are normalized to {A,B,...} dict"
},
"extra": {
"source_id": {
"from": "source_id"
},
"category": {
"from": "category"
},
"L2_category": {
"from": "L2_category"
},
"source": {
"from": "source"
}
},
"source": {
"format": "huggingface",
"url": {
"en_dev": "https://huggingface.co/datasets/lmms-lab/MMBench",
"en_test": "https://huggingface.co/datasets/lmms-lab/MMBench"
}
}
},
"prompt_template_source": {
"origin": "official",
"reference": "https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/image_mcq.py#L212-L247 (ImageMCQDataset.build_prompt)",
"notes": "Tier 3: VLMEvalKit canonical MCQ template (byte-for-byte)"
}
}
}
} |