{"schema":"record/v1","id":"art_01M61FNQJSX043MCH9C72NZR30","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Qwen-Image-Bench scoring: match prompt IDs, map 0/1/2 to 0/60/100 and average active pillars","summary":"The pinned README documents ID-bound evaluation of generated images, N/A exclusion, hierarchical scores and a Chinese-prompt leaderboard. It separates recomputing stored judge responses from judging new images. Judge controls are documented, but code/checklists, data rows, weight revisions and agreement validation were not inspected or reproduced.","status":"active","body":{"text":"Qwen-Image-Bench evaluates text-to-image outputs with a diagnostic judge. This guide follows dataset README revision d2493deb153b020cf169c7e3f57d15e4dd697038, captured on 2026-10-28. The full text was inspected; the paper, scripts, dataset rows, generated images and judge weights were not. The README retains image-to-text in YAML while describing text-to-image evaluation in its body. It does not explain that metadata choice; it does not establish a broken generation interface.\n\nThe documented benchmark has 1,000 bilingual prompt records, balanced between 500 long and 500 short prompts. Those counts describe prompt length, not a split of 500 Chinese versus 500 English records. It organizes evaluation into five pillars, 23 intermediate capabilities and 56 facets. Quality, aesthetics and alignment are joined by real-world fidelity and creative generation. Text rendering and visual storytelling are named parts of the taxonomy; this does not establish a tested metric for subject preservation across edits or shots.\n\nThe dataset configuration names a test split in qwen_image_bench_hf_v0518.jsonl. Its described rows contain bilingual prompt and dimension fields, a relative generated-image path for each model, and per-model raw judge responses. Those fields were read in the README, not checked against actual rows. A separate ground-truth reference-image schema and the generators' exact call settings are not established by this document.\n\nFor your own images, the documented input is CSV, JSON or JSONL with ID, prompt and image_path. ID must match the benchmark's 1–1000 metadata IDs, which select the dimensions applicable to that prompt. The default metadata file is metadata/bench_metadata.json; --local-metadata can override it. The README's example calls python judge.py --input your_data.jsonl --model Qwen/Qwen-Image-Bench. --model is required; the example model ID is not a default or a pinned weight revision. The model namespace Qwen/Qwen-Image-Bench and dataset namespace datasets/Qwen/Qwen-Image-Bench are separate resources. This contract does not justify attaching an existing ID to an unrelated production prompt; inspect the metadata and checklists before extending the task.\n\nScoring maps raw 0 to 0, raw 1 to 60 and raw 2 to 100. N/A is excluded. It is not a linear rescaling in which raw 1 becomes 50, and an inapplicable facet is not a failed facet. The README describes bottom-up aggregation from facets through capabilities to pillars. Each sample's overall score is the unweighted mean of its active pillars, three to five per prompt; the model score averages the 1,000 prompt-level scores. A flat average over every facet, a zero-filled inactive pillar or an average over a different prompt subset is therefore not the documented overall protocol. Exact lower-level weighting, parsing and correction behavior still require the implementation.\n\nThere are two documented paths. To recompute scores from already stored responses, the README shows python compute_scores.py --input qwen_image_bench_hf_v0518.jsonl and reports scores_result.xlsx plus scores_detail.json. To judge new images, judge.py writes per-row judged CSV/JSONL with the original fields, combined judge_model_output and per-pillar raw outputs, plus aggregate bench_scores JSON and Excel files. Recomputing stored responses is distinct from rerunning the judge or regenerating images. These commands and outputs are documentation, not a successful run here.\n\nThe documented judge parameter set is seed42, temperature0, top_k1, top_p1.0, repetition_penalty1.05, max_new_tokens4096, enable_thinking true and max_batch_size24. These are judge inference settings, not the source image generators' settings. The CLI exposes batch size and token controls, so the table does not prove a hard batch limit or runtime equivalence after overrides. The README describes a Qwen3.6-27B-based judge and reports expert annotation and Spearman rho0.92 agreement. That is an author-reported result; this text does not supply enough held-out validation detail, uncertainty or release pins to establish independent production reliability.\n\nThe shown leaderboard evaluates images made from Chinese prompts. English leaderboard results are described as a future release in this snapshot. Bilingual prompt fields do not make those scores bilingual, and the named model labels do not identify immutable API/model versions or generation settings. Treat its rankings as source-reported snapshot results rather than current model recommendations. A rubric score also does not by itself certify client acceptance, legal safety, bias absence or temporal consistency.\n\nBefore a reproducible evaluation, inspect and pin judge.py, score_utils.py, checklists.py, bench_metadata.json, requirements and the backend, then bind the judge weights, prompt language, exact prompt IDs, generated-image hashes and generation conditions. Preserve raw judge responses and parse failures; compare only a matched protocol. This is a proposed verification sequence, not executed work. The README names these files but their contents and enforcement of ID/rubric matching remain uninspected.\n\nThe card declares Apache-2.0. Full license documents and per-component terms were not inspected; the declaration does not clear all generated images, source data, model weights or service outputs. This record is original method analysis and citations, with no source-media or dataset-row republication. Evidence is source_inspected / not_run / unreviewed. Local Qwen extraction and its support checks are provisional same-operator work; the frontier text review does not constitute an outside independent attestation.","citations":[{"url":"https://huggingface.co/datasets/Qwen/Qwen-Image-Bench/raw/d2493deb153b020cf169c7e3f57d15e4dd697038/README.md","note":"Full pinned README text; exact line/byte/excerpt hashes in source pins. Linked assets and implementation not inspected."}]},"categories":["T05","T08"],"tags":["qwen-image-bench","image-evaluation","scoring","judge","reproducibility"],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[],"execution_record_ids":[]},"rights":[{"notes":"Original attributed method analysis and functional schema/settings only. Source text, generated images, rows, code and weight rights remain separate.","hosting":"permitted","component":"documentation","license_id":null,"modification":"permitted","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-d3d4f8a10eca8e8e71614c375b593a5f18e474af/record.json#p-d3d4f8a10eca8e8e71614c375b593a5f18e474af/record","import_batch_id":"pipe-9bdcf315d59da715e0cfcfe7950fa3e04c7d1cd3","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-9bdcf315d59da715e0cfcfe7950fa3e04c7d1cd3","original":{"body":{"text":"Qwen-Image-Bench evaluates text-to-image outputs with a diagnostic judge. This guide follows dataset README revision d2493deb153b020cf169c7e3f57d15e4dd697038, captured on 2026-10-28. The full text was inspected; the paper, scripts, dataset rows, generated images and judge weights were not. The README retains image-to-text in YAML while describing text-to-image evaluation in its body. It does not explain that metadata choice; it does not establish a broken generation interface.\n\nThe documented benchmark has 1,000 bilingual prompt records, balanced between 500 long and 500 short prompts. Those counts describe prompt length, not a split of 500 Chinese versus 500 English records. It organizes evaluation into five pillars, 23 intermediate capabilities and 56 facets. Quality, aesthetics and alignment are joined by real-world fidelity and creative generation. Text rendering and visual storytelling are named parts of the taxonomy; this does not establish a tested metric for subject preservation across edits or shots.\n\nThe dataset configuration names a test split in qwen_image_bench_hf_v0518.jsonl. Its described rows contain bilingual prompt and dimension fields, a relative generated-image path for each model, and per-model raw judge responses. Those fields were read in the README, not checked against actual rows. A separate ground-truth reference-image schema and the generators' exact call settings are not established by this document.\n\nFor your own images, the documented input is CSV, JSON or JSONL with ID, prompt and image_path. ID must match the benchmark's 1–1000 metadata IDs, which select the dimensions applicable to that prompt. The default metadata file is metadata/bench_metadata.json; --local-metadata can override it. The README's example calls python judge.py --input your_data.jsonl --model Qwen/Qwen-Image-Bench. --model is required; the example model ID is not a default or a pinned weight revision. The model namespace Qwen/Qwen-Image-Bench and dataset namespace datasets/Qwen/Qwen-Image-Bench are separate resources. This contract does not justify attaching an existing ID to an unrelated production prompt; inspect the metadata and checklists before extending the task.\n\nScoring maps raw 0 to 0, raw 1 to 60 and raw 2 to 100. N/A is excluded. It is not a linear rescaling in which raw 1 becomes 50, and an inapplicable facet is not a failed facet. The README describes bottom-up aggregation from facets through capabilities to pillars. Each sample's overall score is the unweighted mean of its active pillars, three to five per prompt; the model score averages the 1,000 prompt-level scores. A flat average over every facet, a zero-filled inactive pillar or an average over a different prompt subset is therefore not the documented overall protocol. Exact lower-level weighting, parsing and correction behavior still require the implementation.\n\nThere are two documented paths. To recompute scores from already stored responses, the README shows python compute_scores.py --input qwen_image_bench_hf_v0518.jsonl and reports scores_result.xlsx plus scores_detail.json. To judge new images, judge.py writes per-row judged CSV/JSONL with the original fields, combined judge_model_output and per-pillar raw outputs, plus aggregate bench_scores JSON and Excel files. Recomputing stored responses is distinct from rerunning the judge or regenerating images. These commands and outputs are documentation, not a successful run here.\n\nThe documented judge parameter set is seed42, temperature0, top_k1, top_p1.0, repetition_penalty1.05, max_new_tokens4096, enable_thinking true and max_batch_size24. These are judge inference settings, not the source image generators' settings. The CLI exposes batch size and token controls, so the table does not prove a hard batch limit or runtime equivalence after overrides. The README describes a Qwen3.6-27B-based judge and reports expert annotation and Spearman rho0.92 agreement. That is an author-reported result; this text does not supply enough held-out validation detail, uncertainty or release pins to establish independent production reliability.\n\nThe shown leaderboard evaluates images made from Chinese prompts. English leaderboard results are described as a future release in this snapshot. Bilingual prompt fields do not make those scores bilingual, and the named model labels do not identify immutable API/model versions or generation settings. Treat its rankings as source-reported snapshot results rather than current model recommendations. A rubric score also does not by itself certify client acceptance, legal safety, bias absence or temporal consistency.\n\nBefore a reproducible evaluation, inspect and pin judge.py, score_utils.py, checklists.py, bench_metadata.json, requirements and the backend, then bind the judge weights, prompt language, exact prompt IDs, generated-image hashes and generation conditions. Preserve raw judge responses and parse failures; compare only a matched protocol. This is a proposed verification sequence, not executed work. The README names these files but their contents and enforcement of ID/rubric matching remain uninspected.\n\nThe card declares Apache-2.0. Full license documents and per-component terms were not inspected; the declaration does not clear all generated images, source data, model weights or service outputs. This record is original method analysis and citations, with no source-media or dataset-row republication. Evidence is source_inspected / not_run / unreviewed. Local Qwen extraction and its support checks are provisional same-operator work; the frontier text review does not constitute an outside independent attestation.","citations":[{"url":"https://huggingface.co/datasets/Qwen/Qwen-Image-Bench/raw/d2493deb153b020cf169c7e3f57d15e4dd697038/README.md","note":"Full pinned README text; exact line/byte/excerpt hashes in source pins. Linked assets and implementation not inspected."}]},"tags":["qwen-image-bench","image-evaluation","scoring","judge","reproducibility"],"type":"article","title":"Qwen-Image-Bench scoring: match prompt IDs, map 0/1/2 to 0/60/100 and average active pillars","rights":[{"notes":"Original attributed method analysis and functional schema/settings only. Source text, generated images, rows, code and weight rights remain separate.","hosting":"permitted","component":"documentation","modification":"permitted","model_training":"unknown","redistribution":"permitted"}],"summary":"The pinned README documents ID-bound evaluation of generated images, N/A exclusion, hierarchical scores and a Chinese-prompt leaderboard. It separates recomputing stored judge responses from judging new images. Judge controls are documented, but code/checklists, data rows, weight revisions and agreement validation were not inspected or reproduced.","evidence":{"review":"unreviewed","execution":"not_run","inspection":"source_inspected"},"categories":["T05","T08"],"extensions":{"curation/v1":{"curator":"windows-gpt-datasets-primary-20261028-2","revision":1,"topic_ids":[],"confidence":"Full original README cold-verified and read; source protocol checked with exact spans. 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