Add dataset card

README.md

@@ -1,54 +1,169 @@
 ---
-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: 8148164061
-    num_examples: 57623
-  - name: val
-    num_bytes: 1190973439
-    num_examples: 8209
-  download_size: 9391851086
-  dataset_size: 9339137500
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: val
-    path: data/val-*
+license: mit
+task_categories:
+  - object-detection
+tags:
+  - scene-graph-generation
+  - visual-relationship-detection
+  - gqa
+  - coco-format
+language:
+  - en
+pretty_name: GQA — General Question Answering (COCO format)
+size_categories:
+  - 100K<n<1M
 ---
+
+# GQA — General Question Answering (COCO format)
+
+This dataset is the **GQA200** split of
+[the GQA dataset](https://cs.stanford.edu/people/dorarad/gqa/about.html)
+(Hudson et al., 2019), reformatted in standard COCO-JSON format. 
+GQA200 contains the top 200 object categories 
+and 100 relations from the original GQA dataset, selected by frequency in the 
+[Stacked hybrid-attention and group collaborative learning for unbiased scene graph generation 
+paper](https://arxiv.org/abs/2203.09811). This dataset has no official test split since it was used 
+for question answering rather than scene graph generation (for test there is no scene graph annotations).
+
+This version in COCO format 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., 2026](https://arxiv.org/abs/2603.06386)).
+
+---
+
+## Annotation overview
+
+Each image comes with:
+- **Object bounding boxes** — 200 GQA object categories.
+- **Scene-graph relations** — 100 predicate categories connecting pairs of objects as
+  directed `(subject, predicate, object)` triplets.
+
+![Annotation example — val split](gqa200_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 |  57 623 |  775 744           |  238 720   |
+| val   |   8 209 |  110 030           |  33 487    |
+
+---
+
+## Object categories (200)
+
+Top-200 GQA object vocabulary used by the standard SGG split. Full list
+embedded in `dataset_info.description`.
+
+## Predicate categories (100)
+
+Top 100 GQA predicate vocabulary used by the standard SGG split. Full list
+embedded in `dataset_info.description`.
+
+---
+
+## Dataset structure
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 57623
+    }),
+    val: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 8209
+    }),
+})
+```
+
+Each row contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `image` | `Image` | PIL image |
+| `image_id` | `int` | Original GQA200 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/GQA200-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 GQA:
+
+```bibtex
+@inproceedings{hudson2019gqa,
+  title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
+  author={Hudson, Drew A and Manning, Christopher D},
+  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
+  pages={6700--6709},
+  year={2019}
+}
+```
+
+And also the paper that established the GQA-200 split:
+
+```bibtex
+@inproceedings{dong2022stacked,
+  title={Stacked hybrid-attention and group collaborative learning for unbiased scene graph generation},
+  author={Dong, Xingning and Gan, Tian and Song, Xuemeng and Wu, Jianlong and Cheng, Yuan and Nie, Liqiang},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={19427--19436},
+  year={2022}
+}
+```
+
+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
+
+The GQA images and annotations are released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.