Datasets:

Claimentine
/

CoVT_scaleup

task_categories:
  - visual-question-answering
  - image-to-text
tags:
  - chain-of-thought
  - depth-estimation
  - edge-detection
  - segmentation
  - webdataset
size_categories:
  - 500K<n<1M

CoVT_scaleup

Chain-of-thought VQA data for training a model to generate auxiliary depth / edge / segmentation maps as intermediate reasoning steps inside a <think> block, e.g.:

<think> The depth map of the image is [depth map], The edge map of the image
is [edge map], ... The segmentation of the image is [seg map]. </think>
The answer is X

Companion code: Claimentine/CoVT_scaleup on GitHub (BAGEL fine-tuning pipeline, ThinkMorph-style boundary signaling — see that repo's README for the modeling side).

Stats

593,790 records
297 WebDataset shards (shards/shard-00000.tar … shards/shard-00296.tar)
1,942,181 images total (original RGB photo + generated depth map + PiDiNet edge map +, for a subset of records, a SAM3 segmentation map)
~332 GB total

Format

Each shard is a plain tar archive following the WebDataset convention: files sharing the same zero-padded 9-digit key belong to one sample.

000000000.json          # metadata + conversation for this sample
000000000.img0.png      # original RGB photo
000000000.img1.png      # depth map
000000000.img2.png      # PiDiNet edge map
000000000.img3.png      # SAM3 segmentation map (present for most, not all, records)
000000001.json
000000001.img0.png
...

{key}.json is the original CoT record with image rewritten to point at the in-tar filenames instead of absolute cluster paths:

{
  "id": "identity_177477",
  "image": ["000000000.img0.png", "000000000.img1.png", "000000000.img2.png", "000000000.img3.png"],
  "conversations": [
    {"from": "human", "value": "<image>\nQuestion: ..."},
    {"from": "gpt", "value": "<think> The depth map of the image is <image>, The edge map of the image is <image>, ... </think> The answer is ..."}
  ],
  "seg_coverage": 0.3558
}

image[0] is always the original RGB photo; image[1:] are the auxiliary maps in the same order they're referenced by the <image> tags inside the <think> block of the gpt turn. Some records only have 3 images (no segmentation map) — the number of <image> tags in the gpt turn always matches len(image) - 1.

Each shard also has a shard-XXXXX.manifest.json (record count / image count for that shard — packing-time bookkeeping, not needed to load the data).

Loading

import webdataset as wds

url = "https://huggingface.co/datasets/claimentine/CoVT_scaleup/resolve/main/shards/shard-{00000..00296}.tar"
ds = wds.WebDataset(url).decode("pil")

for sample in ds:
    record = sample["json"]
    rgb = sample["img0.png"]
    depth = sample["img1.png"]
    ...

Or download shards directly and use the loader in the companion code repo (data/cot_dataset.py:CoTJSONLIterableDataset expects a plain jsonl + loose image files on disk — extract shards locally first if you want to train with that exact loader unmodified).

Provenance / license note

Source questions/images are drawn from a mixture of existing VQA datasets (TallyQA, CLEVR, A-OKVQA, ST-VQA, and others reachable via the record id prefix, e.g. cauldron/tallyqa/...), each carrying their own upstream license — this repo does not re-license them. Depth maps, PiDiNet edge maps, and SAM3 segmentation maps are generated/derived by this project. No explicit license is declared at the dataset-repo level; treat as research-use data and check the upstream source dataset's license before any other use.