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README.md

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+---
+language:
+- en
+license: unknown
+task_categories:
+- image-segmentation
+- object-detection
+tags:
+- object-detection
+- bounding-boxes
+- relation-ordering
+- ADE20K
+- COCO
+pretty_name: ROPE
+size_categories:
+- 1K<n<10K
+---
+
+# ROPE: Robust Object Pose Estimation through Relation Ordering
+
+This dataset is a reformatted version of [sled-umich/ROPE](https://huggingface.co/datasets/sled-umich/ROPE),
+originally from the paper **"ROPE: Robust Object Pose Estimation through Relation Ordering"**.
+
+## Dataset Description
+
+ROPE is an object detection benchmark that combines images from ADE20K and COCO datasets.
+Each image contains objects with bounding box annotations and object set information.
+The dataset evaluates models on their ability to handle different spatial relationship patterns
+between objects.
+
+### Splits
+
+| Split       | Rows  |
+|-------------|-------|
+| train       | 2,587 |
+| validation  | 2,574 |
+
+### Split Types
+
+Each image belongs to one of five split types based on the spatial relationship pattern:
+
+| Split Type      | Train | Validation | Description                              |
+|-----------------|-------|------------|------------------------------------------|
+| AAAAB           | 168   | 334        | Five objects, one different from the rest |
+| BAAAA           | 168   | 334        | Five objects, first is different          |
+| homogenous      | 400   | 490        | Objects from same category               |
+| heterogenous    | 312   | 246        | Objects from different categories         |
+| mixed           | 1,539 | 1,170      | Mixed category composition               |
+
+### Labels (Source Dataset)
+
+| Source | Train | Validation |
+|--------|-------|------------|
+| ADE    | 1,379 | 1,322      |
+| COCO   | 1,208 | 1,252      |
+
+## Schema
+
+| Column      | Type                        | Description                                                    |
+|-------------|-----------------------------|----------------------------------------------------------------|
+| image       | struct\<bytes, path\>       | The image in HF image format (binary bytes + filename)         |
+| label       | string                      | Source dataset: "ADE" or "COCO"                                |
+| split_type  | string                      | Relationship pattern: "AAAAB", "BAAAA", "homogenous", "heterogenous", or "mixed" |
+| objects     | string (JSON)               | JSON-encoded list of objects with bounding boxes (name, object_id, bndbox) |
+| object_set  | string (JSON)               | JSON-encoded list of all object names present in the image     |
+| relations   | string (JSON)               | JSON-encoded list of spatial relations between objects         |
+| size        | string (JSON)               | JSON-encoded image dimensions (width, height, depth)           |
+
+### Example Objects Format
+
+```json
+[
+  {
+    "name": "person",
+    "object_id": "13",
+    "difficult": "0",
+    "bndbox": {"xmin": 244, "ymin": 0, "xmax": 290, "ymax": 123},
+    "bbox_number": 1
+  }
+]
+```
+
+## Differences from Original
+
+This version differs from the original `sled-umich/ROPE` dataset:
+
+1. **Annotations merged**: The original stored JSON annotation files separately from the image dataset.
+   This version embeds the annotations directly as columns.
+2. **Raw images removed**: The original included both "bbox" (with bounding box overlays) and "raw" (original)
+   versions of each image. This version only includes the bbox images.
+3. **Corrected labels**: The original imagefolder-based labels were all "ADE" due to directory structure.
+   This version uses the actual `data_source` field from annotations.
+4. **Unified schema**: All metadata is available in a single table rather than requiring separate file lookups.
+
+## Citation
+
+```bibtex
+@inproceedings{shan2024rope,
+  title={ROPE: Robust Object Pose Estimation through Relation Ordering},
+  author={Shan, Sijie and others},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  year={2024}
+}
+```

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