Upload VBC visualization results

README.md

@@ -1,5 +1,57 @@
+---
+license: apache-2.0
+task_categories:
+  - image-segmentation
+  - visual-question-answering
+language:
+  - en
+tags:
+  - referring-expression-segmentation
+  - visual-grounding
+  - refcocog
+  - fine-grained-evaluation
+size_categories:
+  - 1K<n<10K
+---
+
 # RefCOCOg-UMD Test Partitioning
 
+This dataset provides **fine-grained evaluation splits** for the RefCOCOg-UMD test set, designed for Referring Expression Comprehension (REC) tasks. Each sample is partitioned by **difficulty level** and **referential type** to enable more detailed performance analysis.
+
+## Dataset Description
+
+- **Total Samples:** 9,482 (by difficulty) / 19,334 (by referential type, with overlaps)
+- **Source:** RefCOCOg-UMD test set
+- **Format:** PyTorch `.pth` files
+
+## Data Structure
+
+Each `.pth` file contains a list of tuples with the following structure:
+
+```python
+import torch
+
+data = torch.load("Easy.pth")
+for item in data:
+    img_name, hw_dict, bbox, phrase, obj_mask = item
+    # img_name: str - Image filename
+    # hw_dict: dict - Height and width info {"height": int, "width": int}
+    # bbox: list - Bounding box [x1, y1, x2, y2]
+    # phrase: str - Referring expression text
+    # obj_mask: tensor/array - Object segmentation mask
+```
+
+## File Organization
+
+### By Difficulty Level
+- `Easy.pth` - Simple referring expressions (4,109 samples)
+- `Medium.pth` - Moderate complexity (4,517 samples)
+- `Hard.pth` - Complex expressions (856 samples)
+
+### By Referential Type × Difficulty
+- `{Type}_Easy.pth`, `{Type}_Medium.pth`, `{Type}_Hard.pth`
+- Types: `Attribute`, `Relation`, `Logic`, `Ambiguity`, `Perspective`
+
 ## Statistics for Each File
 
 <table border="1" style="border-collapse: collapse;">
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     <td>42.79</td>
     <td>1,508</td>
   </tr>
+</table>
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download a specific file
+file_path = hf_hub_download(
+    repo_id="marloweee/BARE_grefumd_test_partitioning",
+    filename="Easy.pth",
+    repo_type="dataset"
+)
+
+# Load and iterate
+data = torch.load(file_path)
+for img_name, hw_dict, bbox, phrase, obj_mask in data:
+    print(f"Image: {img_name}, Query: {phrase}")
+```
+
+## License
 
-</table>
+This dataset is released under the Apache 2.0 License.