---
license: other
license_name: cc3m-terms
license_link: https://ai.google.com/research/ConceptualCaptions/download
task_categories:
  - zero-shot-classification
  - image-to-text
language:
  - en
size_categories:
  - 1M<n<10M
tags:
  - grounding
  - bounding-boxes
  - vision-language
  - cc3m
configs:
  - config_name: annotations
    data_files: annotations-*.parquet
  - config_name: row_map
    data_files: cc3m_row_map.parquet
---

# CC3M grounded annotations

Region-level grounding for Conceptual Captions 3M: bounding boxes, the noun
phrase each box grounds, and the span of the caption that phrase came from, for
3,016,640 of CC3M's 3,318,333 rows.

**No images here.** This is metadata only, joinable onto a CC3M copy you already
have. That is the point of it: the grounding is 354 MB, the pixels are 125 GB.

## Files

| file | rows | size |
| --- | --- | --- |
| `annotations-0000..0482.parquet` | 3,016,640 | 199 MB |
| `cc3m_row_map.parquet` | 3,016,640 | 155 MB |

### `annotations-*.parquet`

| column | type | meaning |
| --- | --- | --- |
| `data_id` | int64 | GLIGEN's sample id, the primary key |
| `caption` | string | the CC3M caption |
| `width`, `height` | int32 | dimensions the boxes are expressed in |
| `boxes_xywh` | list of 4 int32 | boxes as x, y, w, h in pixels of `width` x `height` |
| `phrases` | list of string | the phrase each box grounds, aligned with `boxes_xywh` |
| `tokens_positive` | string | JSON, character spans of each phrase within `caption` |

### `cc3m_row_map.parquet`

| column | type | meaning |
| --- | --- | --- |
| `data_id` | int64 | joins to the annotations |
| `cc3m_row` | int64 | 0-based row in `Train_GCC-training.tsv` |
| `url` | string | that row's image URL, the practical join key |
| `gap` | int32 | rows skipped before this one when aligning, 0 for a clean match |
| `ambiguous` | bool | true where the alignment could not be pinned to one row |

5,191 rows (0.17%) are flagged `ambiguous`; the remaining 99.83% resolve to a
single CC3M row. A further 14,044 resolve to a row but carry an empty `url`, and
are *not* flagged ambiguous, so joining on `url` reaches 3,002,596 samples
(99.53%) and needs that filter separately. 285,709 rows have a nonzero `gap`,
maximum 6.

## Joining it to your CC3M

Join on `url`, not on position: every redistribution of CC3M is a different
subset in a different order, because URLs rot at different rates for everyone.
Expect roughly 90% of your copy to be covered, since GLIGEN grounded 3.0M of the
3.3M rows and your copy is itself a subset.

```python
from huggingface_hub import snapshot_download

path = snapshot_download("freek23/cc3m-grounded-annotations", repo_type="dataset")
```

Boxes are in the coordinate frame given by `width` and `height`, which is the
original image as GLIGEN received it. If your copy was resized to a square, the
boxes must be padded or cropped with it or they will land in the wrong place.

Loader, join and geometry handling: https://github.com/fbyrman/vlm-geometry-ablation

## Provenance

Derived from [`gligen/cc3m_tsv`](https://huggingface.co/datasets/gligen/cc3m_tsv)
at revision `3f8d464e87669c5350787bdbc547f93dbb62357a`, which packages CC3M
images together with GLIGEN's grounding. The boxes and phrases are GLIGEN's; the
contribution here is extracting them without the pixels and recovering which
CC3M row each one belongs to.

That last part was necessary because GLIGEN's `data_id` indexes their own
downloaded subset, not `Train_GCC-training.tsv`, so the annotations could not be
joined onto any other CC3M copy as published. The two caption sequences are
walked together to recover the mapping, and it was checked by re-downloading
sampled URLs and confirming their dimensions agree with the recorded ones.

## Licensing

The boxes and phrases come from GLIGEN, the captions and URLs from CC3M, and
both carry their originators' terms. CC3M's terms govern the captions and URLs
redistributed here; see the license link above. No image data is included.

## Citation

Cite GLIGEN and Conceptual Captions, which produced the underlying annotations
and dataset:

```bibtex
@inproceedings{li2023gligen,
  title     = {{GLIGEN}: Open-Set Grounded Text-to-Image Generation},
  author    = {Li, Yuheng and Liu, Haotian and Wu, Qingyang and Mu, Fangzhou and
               Yang, Jianwei and Gao, Jianfeng and Li, Chunyuan and Lee, Yong Jae},
  booktitle = {CVPR},
  year      = {2023}
}

@inproceedings{sharma2018conceptual,
  title     = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset
               For Automatic Image Captioning},
  author    = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
  booktitle = {ACL},
  year      = {2018}
}
```