Add dataset card

README.md

@@ -1,59 +1,212 @@
 ---
-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: 810556127
-    num_examples: 9538
-  - name: val
-    num_bytes: 85408782
-    num_examples: 733
-  - name: test
-    num_bytes: 514138017
-    num_examples: 4403
-  download_size: 1866283688
-  dataset_size: 1410102926
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: val
-    path: data/val-*
-  - split: test
-    path: data/test-*
+license: cc-by-4.0
+task_categories:
+  - object-detection
+tags:
+  - scene-graph-generation
+  - visual-relationship-detection
+  - visual-genome
+  - coco-format
+language:
+  - en
+pretty_name: IndoorVG — Indoor Visual Genome (COCO format)
+size_categories:
+  - 10K<n<100K
 ---
+
+# IndoorVG — Indoor Visual Genome (COCO format)
+
+**IndoorVG** is a curated split of
+[Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html)
+targeting real-world **indoor** scenarios (kitchens, offices, living rooms, …).
+It was proposed in
+[Neau et al. (2024)](https://link.springer.com/chapter/10.1007/978-3-031-55015-7_25)
+and reformatted here in standard COCO-JSON format.
+
+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://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_239/paper.pdf)).
+
+The 84 object classes and 37 predicate classes were **manually selected and
+semi-automatically merged** to reduce label noise and ambiguity compared to VG150,
+focusing on indoor-relevant concepts.
+
+---
+
+## Annotation overview
+
+Each image comes with:
+- **Object bounding boxes** — 84 indoor-focused object categories.
+- **Scene-graph relations** — 37 predicate categories connecting pairs of objects as
+  directed `(subject, predicate, object)` triplets.
+
+![Annotation example — val split](indoorvg_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 |  9 538 |  125 411           |  72 291   |
+| val   |    733 |   10 246           |   4 866   |
+| test  |  4 403 |   61 278           |  29 367   |
+
+---
+
+## Object categories (84)
+
+Manually curated indoor vocabulary: *bag, basket, bin, blind, book, bottle, bowl,
+cabinet, ceiling, chair, …* Full list embedded in `dataset_info.description`.
+
+## Predicate categories (37)
+
+> above · against · at · attached to · behind · between · carrying · covering ·
+> cutting · drinking · eating · filled with · for · hanging from · has · holding ·
+> in · in front of · laying on · looking at · lying on · mounted on · near · of ·
+> on · playing with · reading · sitting at · sitting on · standing on · taking ·
+> talking on · under · using · watching · wearing · with
+
+---
+
+## Dataset structure
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 9538
+    }),
+    val: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 733
+    }),
+    test: Dataset({
+        features: ['image', 'image_id', 'width', 'height', 'file_name',
+                   'objects', 'relations'],
+        num_rows: 4403
+    }),
+})
+```
+
+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/IndoorVG-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"])
+```
+
+This dataset can be used with the pycocotools API for scene graph generation:
+```bash
+pip install git+https://github.com/Maelic/pycocotools
+```
+
+```python
+from pycocootools.coco import COCO
+
+from datasets import load_dataset
+ds = load_dataset("maelic/IndoorVG-coco-format")
+# Convert Hugging Face dataset to COCO format
+coco_ds = {
+    "images": ds["train"]["image_id"],
+    "annotations": ds["train"]["objects"],
+    "rel_annotations": ds["train"]["relations"],
+    "categories": json.loads(ds["train"].info.description)["categories"],
+    "rel_categories": json.loads(ds["train"].info.description)["rel_categories"],
+}
+coco = COCO()
+coco.dataset = coco_ds
+coco.createIndex()
+
+for img_id in coco.getImgIds():
+    rel_ids = coco.getRelIds(imgIds=img_id)
+    relations.extend(coco.loadRels(rel_ids))
+```
+---
+
+## Citation
+
+If you use this dataset, please cite the IndoorVG paper:
+
+```bibtex
+@incollection{neau2023defense,
+  title={In defense of scene graph generation for human-robot open-ended interaction in service robotics},
+  author={Neau, Ma{"e}lic and Santos, Paulo and Bosser, Anne-Gwenn and Buche, C{'e}dric},
+  booktitle={Robot World Cup},
+  pages={299--310},
+  year={2023},
+  publisher={Springer}
+}
+```
+
+And 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 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.