license: other
task_categories:
- image-to-image
tags:
- retrieval
- composed-image-retrieval
configs:
- config_name: web_search
data_files:
- split: train
path: pairs/category=web_search/*.parquet
- config_name: faiss_legacy
data_files:
- split: train
path: pairs/category=faiss_legacy/*.parquet
- config_name: train-geminicot-20k
data_files:
- split: train
path: cot20k/pairs/category=train-geminicot-20k/*.parquet
- config_name: train-qwencot-20k
data_files:
- split: train
path: cot20k/pairs/category=train-qwencot-20k/*.parquet
retdata: reasoning-based composed image retrieval pairs
Query image + instruction -> target image, where the target follows from the query by an
inference rather than visual similarity. Rows are the judge-accepted (validity.keep)
set of the general pipeline, packed in the Qwenvl2d5 v2 pair format with images inline.
| subset | rows | dropped (image unreadable) |
|---|---|---|
web_search |
153,883 | 0 |
faiss_legacy |
1,558,022 | 0 |
pairs/category=<subset>/ holds the training rows: query, cand, neg structs with
conversations, data_path (""), image (Image(decode=False), the original bytes) and
video (empty), as in williamium/mmret-ume-sft-v2. image.path is the source-relative
path (S3 <bucket>/<key> or data/gsearch_targets/<file>) for traceability; on the
candidate side it is the canonical path of the image's content (the smallest path among
targets with identical bytes), so the training collator's path-based candidate id treats a
target reused by several queries as one candidate. Queries use the no-think system prompt
and <think></think>\n<emb>; neg is null. meta/<subset>/ has one row per training row
(same file name and order): the row's own source paths, blake3 content keys (q_key,
pos_key), instruction, caption, rationale, keywords. Some images are RGBA: decode them
compositing onto white, not with .convert("RGB") (which turns transparency black).
CoT subsets
train-geminicot-20k and train-qwencot-20k hold the same 20,000 rows of the two subsets above, drawn as random 100-row groups. Queries use the reasoning system prompt, with a chain of thought written by Gemini or the Qwen writer before <emb>. The writers saw the query image, the instruction and the target caption as a soft reference, not the target image. In train-qwencot-20k, 6,033 rows were written with the writer's thinking mode on (think in the metadata; only the final answer is kept) and 13,967 with it off. cot20k/meta/ holds each row's source, CoT and model. Files live under cot20k/ so that pairs/category=* still means the full data.
Rows that repeated 8 or more consecutive caption words were rewritten at a higher temperature; temperature in the metadata is 0 for the model default.