--- license: other license_name: mixed-per-image-licenses 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, '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. ![An image with bounding boxes drawn over detected objects, each box captioned, alongside the image's global caption](./example_figure.png) - **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) (e.g. GLIGEN [Li et al., 2023], InstanceDiffusion [Wang et al., 2024]), and the associated datasets (e.g. ROVI [Peng et al., 2025]). 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. | Which of the four source datasets each row came from is published separately, as `metadata/source_dataset_index.parquet` — see [Which rows come from which source](#which-rows-come-from-which-source). ### 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 This dataset aggregates images from multiple public sources, each under its own terms. They fall into three groups, not one blanket restriction: - **No research-only restriction — usable for any purpose, including commercial, per the source's own license (with attribution where required):** - The bounding boxes and captions contributed by Breakpoint AI itself — **CC BY 4.0**. - Images from **Open Images** — CC BY 2.0 (images), CC BY 4.0 (Google's annotations). - Images from **Wikimedia Commons** — CC BY-SA, CC BY, CC0, or public domain, per file; check the specific file's license for attribution/share-alike requirements. - **Restricted to non-commercial research use:** - Images from **RedCaps** — under the RedCaps terms, non-commercial research only. - **Rights not cleared by Breakpoint — neither confirmed open nor research-restricted, genuinely case-by-case:** - Images from **LAION** — the URLs/metadata are CC BY 4.0, but each image remains under its original, third-party owner's rights, which Breakpoint has not individually verified. Downstream users are responsible for determining the applicable rights before using these images beyond research. ### Which rows come from which source | Source | Rows | Share | Licensing group (above) | |--------|-----:|------:|-------------------------| | LAION | 37,392,932 | 68.10% | Rights not cleared — case-by-case | | Wikimedia Commons | 7,344,982 | 13.38% | No research-only restriction | | RedCaps | 5,290,815 | 9.64% | Non-commercial research only | | Open Images | 4,880,748 | 8.89% | No research-only restriction | The rows themselves don't carry a source column, but **`metadata/source_dataset_index.parquet`** in this repo maps every row to its source. It has one row per dataset row, in the same order as the `train` split, with columns `index` (int32) and `source_dataset` (one of `laion`, `wikimedia`, `redcaps`, `openimages`). ```python from datasets import load_dataset from huggingface_hub import hf_hub_download import pyarrow.parquet as pq ds = load_dataset("BreakpointAI/breakpoint-grounding-55m", split="train") path = hf_hub_download( repo_id="BreakpointAI/breakpoint-grounding-55m", repo_type="dataset", filename="metadata/source_dataset_index.parquet", ) sources = pq.read_table(path).column("source_dataset").to_pylist() ds = ds.add_column("source_dataset", sources) # now filterable redcaps_only = ds.filter(lambda r: r["source_dataset"] == "redcaps") ``` The join is positional, so it only holds if you haven't shuffled or filtered the dataset before attaching the column. Use of the images is governed by the applicable source's terms above; downstream users are responsible for complying with them. **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 - **Object detection**: bounding boxes and class labels were produced by an open-vocabulary detector (Grounding DINO) as an upstream step. - **Per-object captions**: [InternVL3-2B](https://huggingface.co/OpenGVLab/InternVL3-2B), served via LMDeploy (TurboMind engine, `internvl2_5` chat template). Each detected box was cropped (no padding) and captioned with the prompt: > `` Describe this `` in 20 words or less and focus on its color, > appearance, size, and style, etc. where `` is the detector's class label for that box. Generated with `max_new_tokens=64`. - **Image-level captions**: [InternVL3-8B](https://huggingface.co/OpenGVLab/InternVL3-8B) (via `transformers`), prompted with: > `` Write a single, natural-language description of this image. Do not use bullet > points or lists. Keep it under 100 words. Generated with `max_new_tokens=128`, sampling enabled. ## Citation ```bibtex @misc{breakpoint_grounding_55m, title = {Breakpoint Grounding 55M}, author = {Wang, Franklin and Zhong, Desmond and Murdoch, Jamie}, year = {2026}, url = {https://huggingface.co/datasets/BreakpointAI/breakpoint-grounding-55m} } ``` ### 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} } ```