| --- |
| license: other |
| license_name: research-use |
| license_link: LICENSE |
| pretty_name: Breakpoint Grounding 55M |
| task_categories: |
| - object-detection |
| - text-to-image |
| - image-to-text |
| language: |
| - en |
| tags: |
| - object-detection |
| - bounding-boxes |
| - grounding |
| - image-captioning |
| - synthetic-data |
| - diffusion-models |
| size_categories: |
| - 10M<n<100M |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: image_caption |
| dtype: string |
| - name: object_captions |
| sequence: string |
| - name: normalized_boxes |
| sequence: |
| sequence: float32 |
| - name: img_size_wh |
| sequence: int32 |
| splits: |
| - name: train |
| num_examples: 1000000 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| # Breakpoint Grounding 55M |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("BreakpointAI/breakpoint-grounding-55m", split="train") |
| ds[0] # {'image': <PIL.Image>, 'image_caption': ..., 'object_captions': [...], 'normalized_boxes': [...], 'img_size_wh': [...]} |
| ``` |
|
|
| ## Dataset summary |
|
|
| **Breakpoint Grounding 55M is, to our knowledge, the largest instance-grounded image–text dataset |
| released publicly.** Every image comes with an **image-level caption**, a set of **object bounding |
| boxes**, and **a caption for every box**. It was built by [Breakpoint AI](https://huggingface.co/BreakpointAI) |
| to train a 10B-parameter joint image + bounding-box diffusion model for synthetic object-detection |
| data generation. |
|
|
| <!-- Once the example figure is ready, uncomment and upload it (see update_hub_card.py --asset): |
|  |
| --> |
| |
| - **54,909,477 images**, each with one image-level caption |
| - **511,652,664 bounding boxes** (9.32 per image on average), each with its own caption |
| |
| For comparison, **ROVI** (ICCV 2025), the most recent comparable instance-grounded dataset, labels |
| 1M web images with a similar VLM + open-vocabulary-detector pipeline — this dataset is more than |
| **50× larger**. |
| |
| ## Why this dataset exists |
| |
| At Breakpoint, we trained diffusion models to generate synthetic training data for object |
| detection models. Our ultimate model was a 10-billion-parameter joint diffusion model that |
| generated images and bounding boxes simultaneously. Given 10 labeled images, it could adapt to a |
| new scene and start producing labeled training images. |
| |
| In academia, there has been considerable research on conditional generation (bounding boxes to |
| image) <!-- TODO: cite GLIGEN, InstanceDiffusion, etc. -->, and the associated datasets |
| <!-- TODO: cite -->. While helpful, this work did not touch joint generation, and unfortunately |
| was not at the scale necessary to be actually useful. |
| |
| In order to get a better performing model, we decided to build this dataset, which is over 50 |
| times larger than comparable datasets (e.g. ROVI, ICCV 2025, at 1M images). Having this much data |
| is what allowed us to train a large and high-performing model. |
| |
| ## Image sources |
| |
| Images are drawn from four public web-image collections: |
| |
| | Source | Original image licensing | Notes | |
| |--------|--------------------------|-------| |
| | [Open Images](https://storage.googleapis.com/openimages/web/index.html) | CC BY 2.0 (images), CC BY 4.0 (Google annotations) | Attribution per the Open Images terms. | |
| | [Wikimedia Commons](https://commons.wikimedia.org/) | Mixed free licenses (CC BY-SA, CC BY, CC0, public domain), per file | Attribution and license vary per file. | |
| | [RedCaps](https://redcaps.xyz/) | Reddit-submitted images; released for non-commercial research under the RedCaps terms | Subject to the RedCaps takedown process. | |
| | [LAION](https://laion.ai/) | URL/metadata under CC BY 4.0; images remain under their original owners' rights | Image bytes are redistributed here rather than URLs — see Licensing. | |
| |
| ## How it was built |
| |
| - **Images**: collected from the four sources above, resized to ~1MP, light integrity filtering |
| (corrupt / unreadable files removed). |
| - **Annotations** (`image_caption`, `object_captions`, `normalized_boxes`): generated by |
| Breakpoint AI's automated annotation pipeline (object detection + captioning models). They are |
| **not human-labeled**. See **Appendix: Annotation pipeline** for details. |
|
|
| ## Dataset structure |
|
|
| ### Fields |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `image` | `Image` | The decoded image (embedded in the Parquet files). | |
| | `image_caption` | `string` | A single natural-language caption describing the whole image. | |
| | `object_captions` | `Sequence(string)` | One caption per detected object. Index-aligned with `normalized_boxes`. | |
| | `normalized_boxes` | `Sequence(Sequence(float32))` | One bounding box per detected object, coordinates normalized to `[0, 1]`. Index-aligned with `object_captions`. | |
| | `img_size_wh` | `Sequence(int32)` | Original image size as `[width, height]` in pixels. | |
|
|
| ### Bounding box format |
|
|
| Each box is `[x_min, y_min, x_max, y_max]` (xyxy), with coordinates normalized to `[0, 1]` |
| relative to image width/height. `normalized_boxes[i]` corresponds to `object_captions[i]`. |
|
|
| ### Splits |
|
|
| | Split | Rows | |
| |-------|------| |
| | `train` | 54,909,477 | |
|
|
| ### Example |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("BreakpointAI/breakpoint-grounding-55m", split="train", streaming=True) |
| row = next(iter(ds)) |
| |
| row["image"] # PIL.Image |
| row["image_caption"] # "a photograph of ..." |
| row["object_captions"] # ["a dog", "a red ball", ...] |
| row["normalized_boxes"] # [[0.12, 0.34, 0.56, 0.78], ...] |
| row["img_size_wh"] # [1820, 1024] |
| ``` |
|
|
| ## Licensing and responsible use |
|
|
| **Released for research and educational use only.** |
|
|
| This dataset aggregates images from multiple public sources, each under its own terms (see |
| **Image sources** above). The bounding boxes and captions contributed by Breakpoint AI are |
| released under **CC BY 4.0**. Use of the images is additionally governed by the licenses and |
| terms of their original sources; downstream users are responsible for complying with those, |
| including any attribution requirements (notably for Wikimedia Commons and Open Images) and the |
| RedCaps non-commercial research terms. |
|
|
| **Personal data.** The images depict real, sometimes identifiable, people and places. Material |
| being publicly posted does not remove it from the scope of data-protection laws such as the GDPR, |
| UK GDPR, and CCPA. Do **not** use this dataset to identify, profile, track, surveil, or contact |
| individuals, or to train biometric identification systems. |
|
|
| **No affiliation.** This dataset is not affiliated with, endorsed by, or connected to Google / |
| Open Images, the Wikimedia Foundation, Reddit, or LAION e.V. |
|
|
| **Removal requests.** If you are the rights holder for an image, or are depicted in one, and want |
| it removed, open a discussion on this repository identifying the affected row indices (or attaching |
| the image) and the affected rows will be removed. |
|
|
| ## Limitations and biases |
|
|
| - Annotations are model-generated and contain errors: missed objects, spurious boxes, imprecise |
| coordinates, and caption hallucinations. |
| - Inherits the content distribution and biases of the four source collections, including |
| web-scale skews in geography, language, subject matter, and the demographics of who posts |
| images to Reddit and Wikimedia. |
| - Images vary in aesthetic and resolution quality; only a light integrity filter was applied. |
|
|
| ## Acknowledgements |
|
|
| - Hosted on the Hugging Face Hub with a public dataset storage grant. |
| - Thanks to Daniel van Strien and the Hugging Face datasets team. |
| - Images from Open Images, Wikimedia Commons, RedCaps, and LAION. |
|
|
| ## Appendix: Annotation pipeline |
|
|
| <!-- TODO: fill in — which object detector (Jamie mentioned Grounding DINO), which captioning |
| model(s), and the prompts used for each. Franklin has this detail. --> |
| |
| ## Citation |
|
|
| ```bibtex |
| @misc{breakpoint_grounding_55m, |
| title = {Breakpoint Grounding 55M}, |
| author = {Wang, Franklin and {[Desmond LASTNAME]} and Murdoch, Jamie}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/BreakpointAI/breakpoint-grounding-55m} |
| } |
| ``` |
|
|
| <!-- TODO: confirm Desmond's last name and Jamie's preferred citation name (Jamie Murdoch vs. |
| William Murdoch), and author order (currently Franklin, Desmond, Jamie per Slack). --> |
| |
| ### Related work |
|
|
| ```bibtex |
| @inproceedings{peng2025rovi, |
| title = {ROVI: A VLM-LLM Re-Captioned Dataset for Open-Vocabulary |
| Instance-Grounded Text-to-Image Generation}, |
| author = {Peng, Cihang and Hou, Qiming and Ren, Zhong and Zhou, Kun}, |
| booktitle = {ICCV}, |
| year = {2025} |
| } |
| |
| @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{wang2024instancediffusion, |
| title = {InstanceDiffusion: Instance-level Control for Image Generation}, |
| author = {Wang, Xudong and Darrell, Trevor and Rambhatla, Sai Saketh and Girdhar, Rohit |
| and Misra, Ishan}, |
| booktitle = {CVPR}, |
| year = {2024} |
| } |
| ``` |
|
|
| <!-- TODO: verify these two entries (author order / exact venue) before publishing — drafted |
| from memory, not looked up. --> |
| |
