Add dataset card

README.md

@@ -1,60 +1,174 @@
----
-license: mit
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: image_id
-    dtype: int64
-  - name: width
-    dtype: int64
-  - name: height
-    dtype: int64
-  - name: file_name
-    dtype: string
-  - name: objects
-    list:
-    - name: area
-      dtype: float64
-    - name: bbox
-      list: float64
-    - name: category_id
-      dtype: int64
-    - name: id
-      dtype: int64
-    - name: iscrowd
-      dtype: int64
-    - name: segmentation
-      list: 'null'
-  - name: relations
-    list:
-    - name: id
-      dtype: int64
-    - name: object_id
-      dtype: int64
-    - name: predicate_id
-      dtype: int64
-    - name: subject_id
-      dtype: int64
-  splits:
-  - name: train
-    num_bytes: 7688744155
-    num_examples: 45564
-  - name: val
-    num_bytes: 183114338
-    num_examples: 1000
-  - name: test
-    num_bytes: 366946032
-    num_examples: 2186
-  download_size: 8232889144
-  dataset_size: 8238804525
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: val
-    path: data/val-*
-  - split: test
-    path: data/test-*
----
+---
+license: mit
+task_categories:
+  - object-detection
+tags:
+  - scene-graph
+  - visual-relationship-detection
+  - panoptic-scene-graph
+  - coco-format
+language:
+  - en
+pretty_name: PSG — Panoptic Scene Graph (COCO format)
+size_categories:
+  - 10K<n<100K
+---
+
+# PSG — Panoptic Scene Graph (COCO format)
+
+This dataset is a reformatted version of the **Panoptic Scene Graph (PSG)** benchmark
+([Yang et al., NeurIPS 2022](https://arxiv.org/abs/2207.11247)) in standard COCO-JSON
+format, ready for use with object detection and scene graph generation pipelines.
+
+It was produced as part of the
+[SGG-Benchmark](https://github.com/Maelic/SGG-Benchmark) framework and used to train
+the models described in the **REACT** paper
+([Neau et al., BMVC 2025](https://arxiv.org/abs/2405.16116)).
+
+/!\ Disclaimer: this dataset does NOT contain original segmentation masks, but only 
+bounding boxes and category labels. Thus this is NOT a panoptic dataset, but rather a 
+scene graph dataset that can only be used to train bounding-box-based SGG models. 
+The original PSG dataset can be downloaded from the [PSG project page](https://github.com/Jingkang50/OpenPSG).
+
+
+---
+
+## Annotation overview
+
+Each image comes with:
+- **Object bounding boxes** — 133 COCO object categories.
+- **Scene-graph relations** — 56 predicate categories connecting pairs of objects as
+  directed `(subject, predicate, object)` triplets.
+
+![Annotation example — val split](psg_samples_val.png)
+
+*Four random validation images with bounding boxes (coloured by category) and
+relation arrows (yellow, labelled with the predicate name).*
+
+---
+
+## Dataset statistics
+
+| Split | Images | Object annotations | Relations |
+|-------|-------:|-------------------:|----------:|
+| train | 45 564 | 494 213 | 254 214 |
+| val   |  1 000 |  19 039 |   7 458 |
+| test  |  2 186 |  24 910 |  13 705 |
+
+---
+
+## Object categories (133)
+
+Standard 133-class COCO panoptic vocabulary: *person, bicycle, car, motorcycle,
+airplane, bus, train, truck, boat, traffic light, …* (full list embedded in
+`dataset_info.description`).
+
+## Predicate categories (56)
+
+> over · in front of · beside · on · in · attached to · hanging from · on back of ·
+> falling off · going down · painted on · walking on · running on · crossing ·
+> standing on · lying on · sitting on · flying over · jumping over · jumping from ·
+> wearing · holding · carrying · looking at · guiding · kissing · eating · drinking ·
+> feeding · biting · catching · picking · playing with · chasing · climbing ·
+> cleaning · playing · touching · pushing · pulling · opening · cooking · talking to ·
+> throwing · slicing · driving · riding · parked on · driving on · about to hit ·
+> kicking · swinging · entering · exiting · enclosing · leaning on
+
+---
+
+## Dataset structure
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 45564
+    }),
+    val: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 1000
+    }),
+    test: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 2186
+    }),
+})
+```
+
+Each row contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `image` | `Image` | PIL image |
+| `image_id` | `int` | Original COCO image id |
+| `width` / `height` | `int` | Image dimensions |
+| `file_name` | `str` | Original filename |
+| `objects` | `List[dict]` | `{id, category_id, bbox (xywh), area, iscrowd, segmentation}` |
+| `relations` | `List[dict]` | `{id, subject_id, object_id, predicate_id}` — ids refer to `objects[*].id` |
+
+---
+
+## Usage
+
+```python
+from datasets import load_dataset
+import json
+
+ds = load_dataset("maelic/PSG-coco-format")
+
+# Recover label maps from the embedded metadata
+meta = json.loads(ds["train"].info.description)
+cat_id2name  = {c["id"]: c["name"] for c in meta["categories"]}
+pred_id2name = {c["id"]: c["name"] for c in meta["rel_categories"]}
+
+sample = ds["train"][0]
+image  = sample["image"]          # PIL Image
+for obj in sample["objects"]:
+    print(cat_id2name[obj["category_id"]], obj["bbox"])
+for rel in sample["relations"]:
+    print(rel["subject_id"], "--", pred_id2name[rel["predicate_id"]], "->", rel["object_id"])
+```
+
+---
+
+## Citation
+
+If you use this dataset, please cite the original PSG paper:
+
+```bibtex
+@inproceedings{yang2022panoptic,
+  title        = {Panoptic scene graph generation},
+  author       = {Yang, Jingkang and Ang, Yi Zhe and Guo, Zujin and Zhou, Kaiyang
+                  and Zhang, Wayne and Liu, Ziwei},
+  booktitle    = {European conference on computer vision},
+  pages        = {178--196},
+  year         = {2022},
+  organization = {Springer},
+}
+```
+
+And the REACT paper if you use the SGG-Benchmark models:
+
+```bibtex
+@inproceedings{Neau_2025_BMVC,
+  author    = {Ma\"elic Neau and Paulo Eduardo Santos and Anne-Gwenn Bosser
+               and Akihiro Sugimoto and Cedric Buche},
+  title     = {REACT: Real-time Efficiency and Accuracy Compromise for Tradeoffs
+               in Scene Graph Generation},
+  booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025,
+               Sheffield, UK, November 24-27, 2025},
+  publisher = {BMVA},
+  year      = {2025},
+  url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_239/paper.pdf},
+}
+```
+
+---
+
+## License
+
+This dataset inherits the **MIT** license of the original PSG benchmark.
+See the [MIT License](https://opensource.org/licenses/MIT) for details.