feat(README): add dataset structure notes

README.md

@@ -60,6 +60,48 @@ Project page: https://sphere-vlm.github.io/
 This version of the dataset was prepared by combining the [JSON annotations](https://github.com/zwenyu/SPHERE-VLM/tree/main/eval_datasets/coco_test2017_annotations) with the corresponding images from [MS COCO-2017](https://cocodataset.org).
 The script used can be found at `prepare_parquet.py`, to be executed in the root of [our GitHub repository](https://github.com/zwenyu/SPHERE-VLM). 
 
+## Dataset Structure
+
+The dataset is split into the following subsets:
+
+1. **Single-skill**:
+    1. **Position** (`position_only`) - 357 samples
+        - Egocentric: 172, Allocentric: 185
+    2. **Counting** (`counting_only-paired-distance_and_counting` + `counting_only-paired-position_and_counting`) - 201 samples
+        - The `counting_only-paired-distance_and_counting` subset comprises questions corresponding to those in `distance_and_counting`, and similarly for `counting_only-paired-position_and_counting` with `position-and_counting`.
+        - For instance, every question in `distance_and_counting` (e.g. "How many crows are on the railing farther from the viewer?") has a corresponding question in `counting_only-paired-distance_and_counting` to count all such instances (e.g. "How many crows are in the photo?")
+    3. **Distance** (`distance_only`) - 202 samples
+    4. **Size** (`size_only`) - 198 samples
+2. **Multi-skill**:
+    1. **Position + Counting** (`position_and_counting`) - 169 samples
+        - Egocentric: 64, Allocentric: 105
+    2. **Distance + Counting** (`distance_and_counting`) - 158 samples
+    3. **Distance + Size** (`distance_and_size`) - 199 samples
+3. **Reasoning**:
+    1. **Object occlusion** (`object_occlusion`) - 402 samples
+        - Intermediate: 202, Final: 200
+        - The `object_occlusion_w_intermediate` subset contains final questions with intermediate answers prefixed in the following format:
+            > "Given that for the question: \<intermediate step question\> The answer is: \<intermediate step answer\>. \<final step question\> Answer the question directly."
+        - For instance, given the two questions "Which object is thicker?" (intermediate) and "Where can a child be hiding?" (final) in `object_occlusion`, the corresponding question in `object_occlusion_w_intermediate` is:
+            > "Given that for the question: Which object is thicker? Fire hydrant or tree trunk? The answer is: Tree trunk. Where can a child be hiding? Behind the fire hydrant or behind the tree? Answer the question directly."
+    3. **Object manipulation** (`object_manipulation`) - 399
+        - Intermediate: 199, Final: 200
+
+### Data Fields
+
+The data fields are as follows:
+
+- `question_id`: A unique ID for the question.
+- `question`: Question to be passed to the VLM.
+- `option`: A list of options that the VLM can select from. For counting tasks, this field is left as null.
+- `answer`: The expected answer, which must be either one of the strings in `option` (for non-counting tasks) or a number (for counting tasks).
+- `metadata`:
+  - `viewpoint`: Either "allo" (allocentric) or "ego" (egocentric).
+  - `format`: Expected format of the answer, e.g. "bool" (boolean), "name", "num" (numeric), "pos" (position).
+  - `source_dataset`: Currently, this is "coco_test2017" ([MS COCO-2017](https://cocodataset.org)) for our entire set of annotations.
+  - `source_img_id`: Source image ID in [MS COCO-2017](https://cocodataset.org).
+  - `skill`: For reasoning tasks, a list of skills tested by the question, e.g. "count", "dist" (distance), "pos" (position), "shape", "size", "vis" (visual).
+
 ## Licensing Information
 
 Please note that the images are subject to the [Terms of Use of MS COCO-2017](https://cocodataset.org/#termsofuse):