| { |
| "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)" |
| } |
| } |
| } |
| } |
