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README.md

@@ -1,35 +1,85 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  - name: width
-    dtype: int64
-  - name: height
-    dtype: int64
-  - name: class_labels
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 1176277993.424
-    num_examples: 2236
-  - name: val
-    num_bytes: 167814270.0
-    num_examples: 319
-  - name: test
-    num_bytes: 338259024.0
-    num_examples: 643
-  download_size: 742525395
-  dataset_size: 1682351287.424
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: val
-    path: data/val-*
-  - split: test
-    path: data/test-*
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- image-segmentation
+- planetary-science
+task_ids:
+- semantic-segmentation
+pretty_name: conequest
 ---
+
+# conequest
+
+A segmentation dataset for planetary science applications.
+
+## Dataset Metadata
+
+* **License:** CC-BY-4.0 (Creative Commons Attribution 4.0 International)
+* **Version:** 1.0
+* **Date Published:** 2025-05-09
+* **Cite As:** TBD
+
+## Classes
+
+This dataset contains the following classes:
+
+- 0: Background
+- 1: Cone
+
+## Directory Structure
+
+The dataset follows this structure:
+
+```
+dataset/
+  ├── train/
+  │   ├── images/  # Image files
+  │   └── masks/   # Segmentation masks
+  ├── val/
+  │   ├── images/  # Image files
+  │   └── masks/   # Segmentation masks
+  ├── test/
+  │   ├── images/  # Image files
+  │   └── masks/   # Segmentation masks
+```
+
+## Statistics
+
+- train: 2236 images
+- val: 319 images
+- test: 643 images
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("gremlin97/conequest")
+```
+
+## Format
+
+Each example in the dataset has the following format:
+
+```
+{
+  'image': Image(...),      # PIL image
+  'mask': Image(...),       # PIL image of the segmentation mask
+  'width': int,             # Width of the image
+  'height': int,            # Height of the image
+  'class_labels': [str,...] # List of class names present in the mask
+}
+```