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README.md

@@ -25,4 +25,117 @@ configs:
   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
+
+```python
+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](https://huggingface.co/datasets/BreakpointAI/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.
+
+**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 |
+
+<!-- TODO: verify descriptions/full names above, especially UWISOccludedDataset and LOCO, whose
+     provenance we're least certain about. Percentages from an earlier per-source row count. -->
+
+## How it was built
+
+- **Object detection**: bounding boxes and class labels were produced by an open-vocabulary
+  detector (Grounding DINO) as an upstream step, on top of the source datasets' images.
+- **Per-object captions**: [InternVL3-2B](https://huggingface.co/OpenGVLab/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](https://huggingface.co/OpenGVLab/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.
+
+Annotations are **not human-labeled** and contain the usual model-generated errors: missed
+objects, spurious boxes, imprecise coordinates, caption 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
+
+```bibtex
+@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}
+}
+```