| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pretty_name: PanoCaps |
| task_categories: |
| - image-segmentation |
| - image-to-text |
| size_categories: |
| - 1K<n<10K |
| dataset_info: |
| features: |
| - name: split |
| dtype: string |
| - name: image_id |
| dtype: string |
| - name: file_name |
| dtype: string |
| - name: image_info |
| struct: |
| - name: data_source |
| dtype: string |
| - name: file_name |
| dtype: string |
| - name: height |
| dtype: int64 |
| - name: id |
| dtype: string |
| - name: width |
| dtype: int64 |
| - name: caption_info |
| struct: |
| - name: caption |
| dtype: string |
| - name: caption_ann |
| dtype: string |
| - name: id |
| dtype: int64 |
| - name: image_id |
| dtype: string |
| - name: label_matched |
| list: |
| - name: mask_ids |
| sequence: int64 |
| - name: txt_desc |
| dtype: string |
| - name: labels |
| sequence: string |
| - name: mask_annotations |
| list: |
| - name: area |
| dtype: int64 |
| - name: bbox |
| sequence: float64 |
| - name: category_id |
| dtype: int64 |
| - name: id |
| dtype: int64 |
| - name: image_id |
| dtype: string |
| - name: iscrowd |
| dtype: int64 |
| - name: segmentation |
| struct: |
| - name: counts |
| dtype: string |
| - name: size |
| sequence: int64 |
| - name: thing_or_stuff |
| dtype: string |
| - name: categories |
| list: |
| - name: id |
| dtype: int64 |
| - name: name |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 29443350 |
| num_examples: 2070 |
| - name: val |
| num_bytes: 4782919 |
| num_examples: 420 |
| - name: test |
| num_bytes: 10976834 |
| num_examples: 980 |
| download_size: 25273455 |
| dataset_size: 45203103 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: val |
| path: data/val-* |
| - split: test |
| path: data/test-* |
| --- |
| # PanoCaps: A Human-Annotated Benchmark for Panoptic Grounded Captioning |
|
|
| [](https://arxiv.org/abs/2609.19143) |
| [](https://github.com/sarapieri/panorama_grounding) |
| [](https://huggingface.co/Panorama-grounding) |
| [](https://www.di.ens.fr/willow/research/panorama/) |
|
|
| <p align="center"> |
| <img src="https://huggingface.co/datasets/Panorama-grounding/PanoCaps/resolve/main/panocaps_card.png" |
| width="100%" |
| alt="PanoCaps example: grounded masks, grounded phrases and the grounded caption" /> |
| </p> |
|
|
| PanoCaps is a benchmark for **panoptic grounded captioning**: a model writes a full-scene caption and grounds every mentioned entity, things and stuff alike, to pixel-level masks. |
|
|
| It contains **3,470 images** and **34K panoptic regions**, averaging **~9 grounded entities per image**, with **>99% of regions grounded**. Captions are human-written and verified, cover the entire visible scene, use open-vocabulary descriptions that go beyond category labels, and support **one-to-many** and **many-to-one** text ↔ mask mappings. Masks follow COCO-style panoptic annotations. |
|
|
| For research and non-commercial use only. |
|
|
| See also our cleaned version of the COCONut-PanCap training captions, |
| [COCONut-PanCap Re-captioned](https://huggingface.co/datasets/Panorama-grounding/COCONut-PanCap-Recaptioned). |
|
|
| ## Images |
|
|
| Images are **not** included in this repository. Download them from the source datasets below, or get **both images and annotations, already formatted**, as a single archive from this [Google Drive link](https://drive.google.com/file/d/1iNEuKWJdv1wKKGycP-4hTKHdfmcuQEOB/view?usp=sharing). The annotations reference the images by `file_name` and `id`. |
|
|
| | Dataset | Data Download Link | Associated Publication | |
| |---------|---------------------------------------------|-----------------------------------------| |
| | ADE20K | [ADE20K Download](https://groups.csail.mit.edu/vision/datasets/ADE20K/) | [ADE20K Paper](https://arxiv.org/abs/1608.05442) | |
| | COCONut | [COCONut GitHub](https://github.com/bytedance/coconut_cvpr2024) | [COCONut Paper](https://arxiv.org/abs/2404.08639) | |
| | VIPSeg | [VIPSeg GitHub](https://github.com/VIPSeg-Dataset/VIPSeg-Dataset/) | [VIPSeg Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Miao_Large-Scale_Video_Panoptic_Segmentation_in_the_Wild_A_Benchmark_CVPR_2022_paper.html) | |
|
|
| ## Download |
|
|
| ```bash |
| # annotations only (load_dataset reads the parquet in data/, not these) |
| hf download Panorama-grounding/PanoCaps --repo-type dataset --include "annotations/*" \ |
| --local-dir /path/to/data/PanoCaps |
| |
| # or images + annotations, pre-formatted, from the Google Drive archive above |
| pip install gdown && gdown 1iNEuKWJdv1wKKGycP-4hTKHdfmcuQEOB -O PanoCaps.zip |
| unzip PanoCaps.zip -d /path/to/data/ |
| ``` |
|
|
| ## Formats |
|
|
| The same annotations are published in two formats: |
|
|
| - **`annotations/`** — COCO-style JSON, as `<split>_caption.json` / `<split>_mask.json` pairs for `train`, `val` and `test`. Recommended for **training and evaluation**. Join the two files on `image_id` / `id`. |
| - **`data/`** — parquet, merging caption and mask information into one entry per image. Recommended for **browsing in the Dataset Viewer**. |
|
|
| Note that `load_dataset` reads only `data/`; the JSON annotations have to be downloaded explicitly. |
|
|
| ### Caption files |
|
|
| `annotations[*]`: |
|
|
| | Field | Description | |
| |---|---| |
| | `caption` | Plain natural-language caption. | |
| | `caption_ann` | The same caption with inline `<mask_ids:text>` grounding tags, e.g. `a <5,6,7:row of buses>`. | |
| | `label_matched` | List of `{"mask_ids": [...], "txt_desc": "..."}`, mapping each grounded span to one or more masks. | |
| | `id` | Caption annotation id. | |
| | `image_id` | Matches `images[*].id` and the mask file. | |
| | `labels` | Semantic label of every mask of the image, in mask order (so `len(labels)` equals the number of masks, and labels may repeat). | |
|
|
| **`mask_ids` are positional indices** into that image's list of masks — equivalently, into `labels` — not the global `id` values used in the mask file. The same mask may be referenced by several spans, for instance when an entity is mentioned again later in the caption. |
| |
| ### Mask files |
| |
| `annotations[*]`: |
| |
| | Field | Description | |
| |---|---| |
| | `id` | Region id, unique within the split. | |
| | `image_id` | Links the region to its image and caption. | |
| | `segmentation` | `{"size": [height, width], "counts": ...}`, COCO panoptic RLE. | |
| | `area` | Region area in pixels. | |
| | `bbox` | COCO-format box, `[x, y, width, height]`. | |
| | `thing_or_stuff` | `thing` for object-like regions, `stuff` for background. | |
| |
| Both caption and mask files also carry `images[*]` with `file_name`, `height`, `width`, `id` and `data_source`. |
| |
| ## Annotation |
| |
| Images were curated from ADE20K, COCONut and VIPSeg for visual quality and suitability for dense grounding. Paid professional annotators then wrote long-form scene descriptions covering attributes, relationships and all visible entities, tagged every textual reference with `<mask_ids:description>` markers, and produced the corresponding `label_matched` mapping. A separate quality-control pass verified grounding ids, completeness of region coverage and annotation consistency. |
| |
| The base panoptic masks come from the source datasets; all captions and grounding annotations were created specifically for PanoCaps. |
| |
| ## License |
| |
| This repository merges, normalizes and redistributes content from existing datasets, and is therefore released **strictly for research and non-commercial use**. Commercial use is not permitted, and users must comply with the licenses of each source dataset. |
| |
| ## Citation |
| |
| If you find our work useful for your research, please consider citing our paper: |
| |
| ```bibtex |
| @article{pieri2026panorama, |
| title = {PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection}, |
| author = {Pieri, Sara and Kazakos, Evangelos and Chen, Shizhe and Sivic, Josef and Schmid, Cordelia}, |
| journal = {arXiv preprint arXiv:2609.19143}, |
| year = {2026} |
| } |
| ``` |
| |
