Datasets:

Icey444
/

ret-sample

license: mit
task_categories:
  - image-to-image
  - visual-document-retrieval
language:
  - en
tags:
  - retrieval
  - multimodal
  - composed-image-retrieval
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: pairs_img/train-*.parquet

ret-sample — reasoning-retrieval query→positive pairs

Composed-image-retrieval (CIR) style pairs: a query image + instruction retrieves a target image. Images are embedded directly in the parquet, so nothing external is needed to train or browse.

3,127 pairs · 8 shards · 341 MB
Built for williamium3000/Qwenvl2d5 (ret-streaming-retsample branch) and streamable by it with no S3 credentials.

Columns

column	type	meaning
`q_image`	`Image()`	query image (PIL on access; stored as original JPEG bytes)
`pos_image`	`Image()`	positive/target image
`instruction`	string	the query instruction (what to retrieve)
`caption`	string	caption of the positive
`q_key`, `pos_key`	string	`blake3(raw file bytes)[:16]` content ids
`group_id`	int	near-duplicate cluster id (teacher cosine ≥ 0.98)
`n_text_tokens`	int	exact instruction length, Qwen2.5-VL tokenizer
`task`, `subset`	string	`cir`; source subset (results / instruct / …)

Usage

from datasets import load_dataset
ds = load_dataset("Icey444/ret-sample", split="train")
r = ds[0]
r["q_image"]        # PIL.Image
r["instruction"]    # "What would I need to use to navigate through a place like this?"
r["pos_image"]      # PIL.Image

Training (in the repo above):

bash scripts/sft_ret_sample_streaming.sh     # --dataset ret_sample%100

Notes

No hard negatives. The training code uses in-batch InfoNCE; its collator explicitly cannot take a 3rd item per instance, so mined negatives would be carried and never read.
Images are stored undecoded and decoded to PIL once at preprocess time — lossless (verified byte-identical through the pipeline) and no per-epoch re-encode.
Source images originate from jeffrey/reasoning_retrieval on the zillion2 store; the _key columns are content hashes of those exact bytes.