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README.md

@@ -1,88 +1,78 @@
 ---
-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: url
-    dtype: string
-  - name: nsid
-    dtype: string
-  - name: objects
-    sequence:
-    - name: id
-      dtype: int64
-    - name: bbox
-      sequence: float64
-      length: 4
-    - name: category_id
-      dtype: int64
-    - name: area
-      dtype: float64
-    - name: iscrowd
-      dtype: int64
-  - name: relations
-    sequence:
-    - name: id
-      dtype: int64
-    - name: annotation_id
-      dtype: string
-    - name: subject_id
-      dtype: int64
-    - name: object_id
-      dtype: int64
-    - name: predicate_id
-      dtype: int64
-    - name: subject_point
-      sequence: float64
-      length: 2
-    - name: object_point
-      sequence: float64
-      length: 2
-  - name: neg_relations
-    sequence:
-    - name: id
-      dtype: int64
-    - name: annotation_id
-      dtype: string
-    - name: subject_id
-      dtype: int64
-    - name: object_id
-      dtype: int64
-    - name: predicate_id
-      dtype: int64
-    - name: subject_point
-      sequence: float64
-      length: 2
-    - name: object_point
-      sequence: float64
-      length: 2
-  splits:
-  - name: train
-    num_bytes: 1154185099.55
-    num_examples: 7645
-  - name: validation
-    num_bytes: 281159864.325
-    num_examples: 1553
-  - name: test
-    num_bytes: 368362974.931
-    num_examples: 2371
-  download_size: 1797859375
-  dataset_size: 1803707938.806
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+license: cc-by-2.0
+task_categories:
+  - object-detection
+tags:
+  - scene-graph-generation
+  - spatial-relations
+  - negative-annotations
+  - spatialsense
+  - coco-format
+language:
+  - en
+pretty_name: SpatialSense (COCO format)
+size_categories:
+  - 10K<n<100K
 ---
+
+# SpatialSense (COCO format)
+
+A mirror of **SpatialSense** (Yang, Russakovsky and Deng, *SpatialSense: An Adversarially
+Crowdsourced Benchmark for Spatial Relation Recognition*, ICCV 2019), reformatted in the same
+COCO-style schema as the other `maelic/*-coco-format` datasets. Every annotation states whether a
+spatial predicate holds between a subject and an object, and the dataset is balanced: half the
+relations are true and half are false.
+
+It is used by axis A6 (spatial relations) of OV-SGG-Bench.
+
+## Source and license
+
+The authors' release is Zenodo record 8104370 (DOI
+[10.5281/zenodo.8104370](https://doi.org/10.5281/zenodo.8104370)), published under **CC BY 2.0**;
+this mirror keeps that license and attributes the authors. Images come from Flickr and the NYU
+Depth dataset, as in the original release. The two source files were checked against the SHA-256
+sums the record publishes (`annotations.json`
+`ae8f613ff1886e27e06f154cc01393c8a86025ae7f8f3664a7fd2308d771bf39`, `images.tar.gz`
+`1f9db2e56f323046aec301288a7e24bd35cce51f651cab93ea2e41dc27b853bd`).
+
+## Contents
+
+| Split | Images |
+|---|---|
+| train | 7,645 |
+| validation | 1,553 |
+| test | 2,371 |
+
+11,569 images, 8,749 true and 8,749 false relations, 9 predicates (`above`, `behind`, `in`,
+`in front of`, `next to`, `on`, `to the left of`, `to the right of`, `under`) and 3,679 free-text
+object names. `categories.json` holds `categories` (the index used by `category_id`) and
+`predicates` (the index used by `predicate_id`), both sorted alphabetically.
+
+| Field | Description |
+|---|---|
+| `image` | original bytes (Flickr JPEG or NYU PNG) |
+| `image_id` | index of the image in the source `annotations.json` |
+| `width`, `height`, `file_name`, `url`, `nsid` | as in the source; `file_name` is the path inside `images.tar.gz` |
+| `objects` | subjects and objects deduplicated by (name, box): `id`, `bbox` (COCO `[x, y, w, h]`, pixels), `category_id`, `area`, `iscrowd` |
+| `relations` | relations labelled **true**: `id`, `annotation_id` (the source `_id`), `subject_id`, `object_id` (indices into `objects`), `predicate_id`, `subject_point`, `object_point` (the `[x, y]` points the annotator clicked) |
+| `neg_relations` | relations labelled **false**, same fields |
+
+## Changes from the source
+
+All mechanical: boxes converted from SpatialSense's `[y_min, y_max, x_min, x_max]` to COCO
+`[x, y, w, h]`; objects deduplicated per image; the `label` field turned into the
+`relations` / `neg_relations` split; macOS `._*` files from the archive skipped; images stored
+with their original bytes. A random sample of 50 images was checked against the source.
+
+## Citation
+
+```bibtex
+@inproceedings{yang2019spatialsense,
+  title     = {SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition},
+  author    = {Yang, Kaiyu and Russakovsky, Olga and Deng, Jia},
+  booktitle = {IEEE/CVF International Conference on Computer Vision (ICCV)},
+  pages     = {2051--2060},
+  year      = {2019},
+  doi       = {10.1109/ICCV.2019.00214}
+}
+```