Datasets:

BreakpointAI
/

breakpoint-grounding-5m

dataset_info:
  features:
    - name: object_captions
      list: string
    - name: image_caption
      dtype: string
    - name: normalized_boxes
      list:
        list: float32
    - name: img_size_wh
      list: int32
    - name: source_dataset
      dtype: string
    - name: relative_path
      dtype: string
  splits:
    - name: train
      num_bytes: 10259224594
      num_examples: 5788300
  download_size: 8572776179
  dataset_size: 10259224594
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
language:
  - en
license: cc-by-4.0
size_categories:
  - 1M<n<10M
task_categories:
  - object-detection
pretty_name: Breakpoint Grounding 5M
tags:
  - object-detection
  - bounding-boxes
  - grounding
  - image-captioning

Breakpoint Grounding 5M

Quick start

from datasets import load_dataset
ds = load_dataset("BreakpointAI/breakpoint-grounding-5m", split="train")
ds[0]  # {'source_dataset': ..., 'relative_path': ..., 'image_caption': ..., 'object_captions': [...], 'normalized_boxes': [...], 'img_size_wh': [...]}

Dataset summary

An annotations-only companion to Breakpoint Grounding 55M: image-level captions, object bounding boxes, and a caption for every box, for 5,788,300 images drawn from 16 public object-detection/tracking benchmarks.

In contrast to the 55M dataset, where the bounding box annotations are model-generated, in this dataset the bounding box annotations here are hand-labeled.

No image bytes are included in this release. Several of the source benchmarks (notably ImageNet and Objects365) restrict redistribution of their images. To stay clear of that, this dataset publishes only the annotations Breakpoint authored, plus a source_dataset + relative_path pointer back to each image — usable by anyone who already has their own licensed copy of the relevant source dataset, laid out in its standard directory structure.

5,788,300 images, each with one image-level caption
51,196,466 bounding boxes (8.84 per image on average), each with its own caption

Source datasets

Source	Share	Description
Open Images (train/test/val)	34.7%	Google's large-scale detection/annotation dataset
Objects365 (train/test/unlabeled)	34.8%	Megvii/BAAI large-vocabulary object detection dataset
LaSOT	13.3%	Large-scale single-object tracking benchmark
ImageNet	10.3%	Large-scale image classification dataset (ILSVRC)
COCO (train)	2.0%	Common Objects in Context detection/captioning benchmark
Open Images (test)	1.9%	(see above)
RoboFlow 100	1.7%	Benchmark aggregating 100 diverse object-detection datasets
LVIS (train)	1.7%	Large-vocabulary instance segmentation, built on COCO images
ObjectNet3D	1.6%	Object recognition/pose dataset
BDD100K	1.4%	Berkeley DeepDrive large-scale driving-scene dataset
CrowdHuman	0.3%	Pedestrian / crowd detection benchmark
UWISOccludedDataset	0.15%	Occlusion-focused detection dataset
LOCO	0.1%	Logistics-scene object detection dataset

How it was built

Object detection: bounding boxes and class labels are given by the datasets, provided by human labelers.
Per-object captions: InternVL3-2B, served via LMDeploy. Each detected box was cropped (no padding) and captioned with:

<image> Describe this <category name> in 20 words or less and focus on its color, appearance, size, and style, etc.

where <category name> is the detector's class label for that box.
Image-level captions: InternVL3-8B, prompted with:

<image> Write a single, natural-language description of this image. Do not use bullet points or lists. Keep it under 100 words.

Captions are not human-generated and contain the usual model-generated hallucinations.

Dataset structure

Field	Type	Description
`source_dataset`	`string`	Which of the 16 source benchmarks this image came from.
`relative_path`	`string`	Path to the image within that source dataset's standard layout.
`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: `[x_min, y_min, x_max, y_max]` (xyxy), normalized to `[0, 1]`.
`img_size_wh`	`Sequence(int32)`	Original image size as `[width, height]` in pixels.

Licensing

The annotations in this dataset (image_caption, object_captions, normalized_boxes, img_size_wh) are released by Breakpoint AI under CC BY 4.0.

Images are not included. To use this dataset with actual pixels, obtain each source dataset separately under its own license/terms and join on source_dataset + relative_path. Several sources (ImageNet, Objects365 in particular) restrict commercial use and/or redistribution — check the terms of whichever source(s) you need before using their images.

Removal requests. If you believe your content is represented in these annotations and want the corresponding row removed, open a discussion on this repository.

Citation

@misc{breakpoint_grounding_5m,
  title  = {Breakpoint Grounding 5M},
  author = {Wang, Franklin and Zhong, Desmond and Murdoch, Jamie},
  year   = {2026},
  url    = {https://huggingface.co/datasets/BreakpointAI/breakpoint-grounding-5m}
}