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README.md

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+---
+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 (PANORAMA): Panoptic grounded captioning via mask-guided refinement
+
+[![Paper](https://img.shields.io/badge/ArXiv-Paper-brown)]()
+[![Code](https://img.shields.io/badge/GitHub-Link-orange)](https://github.com/sarapieri/panorama_grounding)
+[![Data](https://img.shields.io/badge/HuggingFace-Data-blue)](https://huggingface.co/datasets/HuggingSara/PanoCaps)
+[![Website](https://img.shields.io/badge/Web-Page-purple)](https://www.di.ens.fr/willow/research/panorama/)
+<!-- [![Model](https://img.shields.io/badge/HuggingFace-Model-green)]() -->
+
+<p align="center">
+  <img src="https://www.di.ens.fr/willow/research/panorama/resources/panorama_teaser.jpg"
+       width="100%"
+       alt="Panorama teaser image" />
+</p>
+
+PanoCaps is a unified dataset for **panoptic grounded captioning**. A model must generate a full-scene caption and ground every mentioned entity (things and stuff) with pixel-level masks. 
+
+Every caption:
+- Is **human-written**
+- Covers the **entire visible scene**  
+- Contains **rich open-vocabulary descriptions** beyond category labels  
+- Includes **inline grounding tags** referring to segmentation masks  
+- Supports **one-to-many** and **many-to-one** text ↔ mask mappings  
+
+This makes PanoCaps suitable for training and evaluating **vision–language models** requiring both detailed scene understanding and fine-grained spatial grounding.
+
+The repository includes:
+
+1. **Raw annotations** in JSON format (`annotations/`) → best for **training & evaluation**  
+2. **A processed Hugging Face dataset**  → best for **visualization & inspection**
+
+This dataset is intended **exclusively for research and non-commercial use**.
+
+## Dataset Details
+
+### Dataset Description
+
+This benchmark supports **panoptic grounded captioning**—a task requiring models to generate long-form, descriptive captions for the entire scene and link all mentioned entities (things and stuff) to pixel-level masks. Masks follow standard **COCO-style panoptic annotations**.
+
+The dataset comprises **3,470 images** with a total of **34K panoptic regions**, averaging **~9 grounded entities per image**. The human-written captions are designed for maximum quality and detail:
+* **Comprehensive:** Covers the entire visible scene.
+* **Open-Vocabulary:** Entity descriptions extend beyond simple category labels.
+* **Fully Grounded:** Uses in-text markers and explicit mapping structures (`label_matched`) to link text spans to masks, ensuring **>99% of regions are grounded**.
+
+
+### Images
+
+**Images are *not* included** in this repository.
+
+To use the dataset, download the original images from the source datasets:
+
+| 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)                      |
+
+
+The JSON annotations reference these images by consistent `file_name` and `id`.
+
+### Repository Structure
+
+<details>
+    <summary>Show Repository Structure</summary>
+    <pre>
+    PanoCaps/
+    │
+    ├── 📁 annotations/
+    │   ├── 📄 test_caption.json
+    │   ├── 📄 test_mask.json
+    │   ├── 📄 train_caption.json
+    │   ├── 📄 train_mask.json
+    │   ├── 📄 val_caption.json
+    │   └── 📄 val_mask.json
+    ├── 📁 data/ (parquet/HF version)
+    └── 📄 README.md
+    </pre>
+</details>
+
+### Recommended Usage
+
+This dataset is provided in two complementary formats:
+
+### **1. Hugging Face Dataset Format (recommended for inspection & visualization)**
+The `train`, `val`, and `test` splits uploaded to the Hugging Face Hub combine **captioning** and **panoptic mask** information into a **single unified entry per image**. This format is ideal for browsing samples interactively in the Dataset Viewer or quick experimentation.
+
+### **2. Original COCO-Style JSON Format (recommended for training & evaluation)**
+Raw annotations are provided under `annotations/` as pairs of Caption files and Mask files (e.g., `train_caption.json` / `train_mask.json`).
+
+These follow the original COCO-style structure and are best suited for:
+- Model training  
+- Model evaluation  
+- Direct integration into COCO-based pipelines  
+
+Caption and mask files can be matched using the shared `image_id` / `id` fields in `images[*]` and `annotations[*]`. 
+
+### Detailed COCO Format
+
+<details>
+  <summary>Show Caption File Example (Structure + Single Entry)</summary>
+
+```javascript
+{
+  "annotations": [
+    {
+      "caption": "The image shows a small, brightly lit bathroom dominated by a white tiled wall...",
+      // Clean natural-language caption
+      "caption_ann": "The image shows a small, brightly lit bathroom dominated by a <0:white tiled wall>...",
+      // Caption with grounded <mask_id:text> references
+      "label_matched": [
+        { "mask_ids": [0], "txt_desc": "white tiled wall" },
+        { "mask_ids": [5], "txt_desc": "white bathtub with chrome faucets" }
+        // ...
+      ],
+      // Mapping text spans → one or more mask IDs
+      // Masks may appear multiple times with different descriptions
+      "id": 0,
+      // Caption annotation ID
+      "image_id": "00000006",
+      // Matches the images[*].id field
+      "labels": ["wall", "floor", "ceiling", "window", "curtain", "tub", "sink"]
+      // All unique semantic labels from the original annotations
+    }
+  ],
+  "images": [
+    {
+      "file_name": "00000006.jpg",
+      // Image filename
+      "height": 973,
+      "width": 512,
+      // Image resolution
+      "id": "00000006",
+      // Image identifier (matches annotation.image_id)
+      "data_source": "ADE20K"
+      // Image source
+    }
+  ]
+}
+```
+</details>
+
+<details>
+  <summary>Show Mask File Example (Structure + Single Entry)</summary>
+
+```javascript
+{
+  "annotations": [
+    {
+      "id": 0,
+      // Unique ID of this panoptic region
+      "image_id": "00000006",
+      // Links this region to the image and caption (matches images[*].id and caption image_id)
+      "category_id": 100,
+      // Semantic category ID (from the original annotations)
+      "segmentation": {
+        "size": [973, 512],
+        // Height and width of the full image (needed to decode the RLE mask)
+        "counts": "d1`1Zk0P2C=C<D=C=C6J=..." 
+        // RLE-encoded mask in COCO panoptic format
+      },
+      "area": 214858,
+      // Number of pixels covered by this segment
+      "bbox": [0.0, 0.0, 511.0, 760.0],
+      // COCO-format bounding box [x, y, width, height]
+      "iscrowd": 0,
+      // 0 for normal segment, 1 if this region is a crowd
+      "thing_or_stuff": "stuff"
+      // Whether this region is an object-like "thing" or background-like "stuff"
+    }
+  ],
+  "images": [
+    {
+      "file_name": "00000006.jpg",
+      // Image file name (in the original dataset)
+      "height": 973,
+      "width": 512,
+      // Image resolution
+      "id": "00000006"
+      // Image identifier (matches annotations[*].image_id and caption image_id)
+      "data_source": "ADE20K"
+      // Image source
+    }
+  ],
+  "categories": [
+    {
+      "id": 1,
+      // Category ID (referenced by annotations[*].category_id)
+      "name": "object"
+      // Human-readable category name
+    }
+  ]
+}
+```
+</details>
+
+---
+
+## Curation and Annotation Details
+
+PanoCaps was built to overcome the limitations of prior grounded captioning datasets (e.g., auto-generated captions, limited vocabulary, and incomplete grounding). Our goal was to create a resource where captions describe every meaningful region using open-vocabulary language, with explicit grounding for each referenced entity.
+The creation process involved four stages:
+
+1.  **Image Selection:** A diverse subset of images was curated from ADE20K, COCONut, and VIPSeg to ensure visual quality and suitability for dense grounding.
+2.  **Captioning:** Professional annotators wrote long-form, fine-grained scene descriptions, highlighting attributes, relationships, and all visible entities.
+3.  **Grounding:** Annotators tagged textual references with `<mask_id:description>` markers and produced **label_matched** structures that map text spans to one or more segmentation masks.
+4.  **Validation:** A second QC stage verified the correctness of grounding IDs, completeness of region coverage, and annotation consistency.
+**Data Producers:** The base panoptic masks were sourced from the original datasets (ADE20K, COCONut, VIPSeg). However, all **captions and grounding annotations** were created specifically for PanoCaps by paid professional annotators following internal guidelines.
+
+---
+
+## License (Research Only)
+Because this repository merges, normalizes, and redistributes content from already existing datasets, the combined dataset is provided **strictly for research and non-commercial use**.
+Commercial use is **not permitted**. Users must comply with the licenses of each original source dataset.
+
+---
+
+## Citation
+If you find our work useful for your research, please consider citing our [paper]():
+```
+@article{YOUR_CITATION_HERE,
+  title={Your Title},
+  author={Your Name},
+  year={2024}
+}
+```
+

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