Add dataset card

README.md

@@ -1,59 +1,190 @@
 ---
-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: 5917400030
-    num_examples: 73538
-  - name: val
-    num_bytes: 645536730
-    num_examples: 4844
-  - name: test
-    num_bytes: 3647823222
-    num_examples: 27032
-  download_size: 15009886603
-  dataset_size: 10210759982
-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-generation
+  - visual-relationship-detection
+  - visual-genome
+  - vg150
+  - coco-format
+language:
+  - en
+pretty_name: VG150 — Visual Genome 150 (COCO format)
+size_categories:
+  - 100K<n<1M
 ---
+
+# VG150 — Visual Genome 150 (COCO format)
+
+This dataset is the standard **VG150** split of
+[Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html)
+(Krishna et al., 2017), the most widely used benchmark for Scene Graph Generation,
+reformatted in standard COCO-JSON format. VG150 contains the top 150 object categories 
+and 50 relations from the original Visual Genome dataset, selected by frequency in the 
+[Scene Graph Generation by Iterative Message Passing paper](https://arxiv.org/abs/1701.02426).
+
+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., BMVC 2025](https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_239/paper.pdf)).
+
+> ⚠️ **Bias warning**: VG150 has been heavily criticised for high class overlap and
+> annotation biases (e.g. *person / man / men / people*). See
+> [VrR-VG (ICCV'19)](https://openaccess.thecvf.com/content_ICCV_2019/html/Liang_VrR-VG_Refocusing_Visually-Relevant_Relationships_ICCV_2019_paper.html)
+> and
+> [Neau et al. (ICCVW'23)](https://openaccess.thecvf.com/content/ICCV2023W/SG2RL/html/Neau_Fine-Grained_is_Too_Coarse_A_Novel_Data-Centric_Approach_for_Efficient_ICCVW_2023_paper.html)
+> for reference.
+
+---
+
+## Annotation overview
+
+Each image comes with:
+- **Object bounding boxes** — 150 Visual Genome object categories.
+- **Scene-graph relations** — 50 predicate categories connecting pairs of objects as
+  directed `(subject, predicate, object)` triplets.
+
+![Annotation example — val split](vg150_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 |  73 538 |  793 061           |  439 063   |
+| val   |   4 844 |   54 415           |   30 133   |
+| test  |  27 032 |  297 922           |  153 509   |
+
+---
+
+## Object categories (150)
+
+Top-150 Visual Genome object vocabulary used by the standard SGG split. Full list
+embedded in `dataset_info.description`.
+
+## Predicate categories (50)
+
+> and · says · belonging to · over · parked on · growing on · standing on · made of ·
+> attached to · at · in · hanging from · wears · in front of · from · for · watching ·
+> lying on · to · behind · flying in · looking at · on back of · holding · between ·
+> laying on · riding · has · across · wearing · walking on · eating · above · part of ·
+> walking in · sitting on · under · covered in · carrying · using · along · with · on ·
+> covering · of · against · playing · near · painted on · mounted on
+
+---
+
+## Dataset structure
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 73538
+    }),
+    val: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 4844
+    }),
+    test: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 27032
+    }),
+})
+```
+
+Each row contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `image` | `Image` | PIL image |
+| `image_id` | `int` | Original Visual Genome 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/VG150-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 Visual Genome:
+
+```bibtex
+@article{krishna2017visual,
+  title={Visual genome: Connecting language and vision using crowdsourced dense image annotations},
+  author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and others},
+  journal={International journal of computer vision},
+  volume={123},
+  number={1},
+  pages={32--73},
+  year={2017},
+  publisher={Springer}
+}
+```
+
+And the original paper that established the VG150 split:
+
+```bibtex
+@inproceedings{xu2017scene,
+  title={Scene graph generation by iterative message passing},
+  author={Xu, Danfei and Zhu, Yuke and Choy, Christopher B and Fei-Fei, Li},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={5410--5419},
+  year={2017}
+}
+```
+
+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
+
+Visual Genome 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.