Add dataset README with metadata

README.md

@@ -1,96 +1,157 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: labels
-    sequence: int8
-  - name: feature_names
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 189511167.738
-    num_examples: 1762
-  - name: val
-    num_bytes: 48685102.0
-    num_examples: 443
-  - name: test
-    num_bytes: 78350787.0
-    num_examples: 739
-  - name: few_shot_train_10_shot
-    num_bytes: 12072390.0
-    num_examples: 128
-  - name: few_shot_train_15_shot
-    num_bytes: 16781171.0
-    num_examples: 175
-  - name: few_shot_train_1_shot
-    num_bytes: 1499051.0
-    num_examples: 16
-  - name: few_shot_train_20_shot
-    num_bytes: 21623151.0
-    num_examples: 220
-  - name: few_shot_train_2_shot
-    num_bytes: 2605778.0
-    num_examples: 30
-  - name: few_shot_train_5_shot
-    num_bytes: 6184131.0
-    num_examples: 67
-  - name: partition_0.01x_partition
-    num_bytes: 2137231.0
-    num_examples: 19
-  - name: partition_0.02x_partition
-    num_bytes: 3740776.0
-    num_examples: 33
-  - name: partition_0.05x_partition
-    num_bytes: 8894127.0
-    num_examples: 81
-  - name: partition_0.10x_partition
-    num_bytes: 20208822.0
-    num_examples: 184
-  - name: partition_0.20x_partition
-    num_bytes: 38839903.0
-    num_examples: 361
-  - name: partition_0.25x_partition
-    num_bytes: 49039006.0
-    num_examples: 447
-  - name: partition_0.50x_partition
-    num_bytes: 96471900.0
-    num_examples: 878
-  download_size: 599828338
-  dataset_size: 596644493.738
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: val
-    path: data/val-*
-  - split: test
-    path: data/test-*
-  - split: few_shot_train_10_shot
-    path: data/few_shot_train_10_shot-*
-  - split: few_shot_train_15_shot
-    path: data/few_shot_train_15_shot-*
-  - split: few_shot_train_1_shot
-    path: data/few_shot_train_1_shot-*
-  - split: few_shot_train_20_shot
-    path: data/few_shot_train_20_shot-*
-  - split: few_shot_train_2_shot
-    path: data/few_shot_train_2_shot-*
-  - split: few_shot_train_5_shot
-    path: data/few_shot_train_5_shot-*
-  - split: partition_0.01x_partition
-    path: data/partition_0.01x_partition-*
-  - split: partition_0.02x_partition
-    path: data/partition_0.02x_partition-*
-  - split: partition_0.05x_partition
-    path: data/partition_0.05x_partition-*
-  - split: partition_0.10x_partition
-    path: data/partition_0.10x_partition-*
-  - split: partition_0.20x_partition
-    path: data/partition_0.20x_partition-*
-  - split: partition_0.25x_partition
-    path: data/partition_0.25x_partition-*
-  - split: partition_0.50x_partition
-    path: data/partition_0.50x_partition-*
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- image-classification
+task_ids:
+- multi-label-image-classification
+pretty_name: MER - Mars Exploration Rover Dataset
 ---
+
+# MER - Mars Exploration Rover Dataset
+
+A multi-label classification dataset containing Mars images from the Mars Exploration Rover (MER) mission for planetary science research.
+
+## Dataset Metadata
+
+* **License:** CC-BY-4.0 (Creative Commons Attribution 4.0 International)
+* **Version:** 1.0
+* **Date Published:** 2025-05-14
+* **Cite As:** TBD
+
+## Classes
+
+This dataset uses multi-label classification, meaning each image can have multiple class labels.
+
+The dataset contains the following classes:
+
+- **rah** (0): Rock Abrasion Tool (RAT) Hole
+- **cla** (1): Clasts
+- **dur** (2): Dunes/Ripples
+- **soi** (3): Soil
+- **roc** (4): Rock Outcrops
+- **clr** (5): Close-up Rock
+- **rab** (6): Rock Abrasion Tool (RAT) Brushed Target
+- **div** (7): Distant Vista
+- **rod** (8): Rover Deck
+- **bso** (9): Bright Soil
+- **flr** (10): Float Rocks
+- **art** (11): Artifacts
+- **pct** (12): Pancam Calibration Target
+- **arh** (13): Arm Hardware
+- **rrf** (14): Rock (Round Features)
+- **sph** (15): Spherules
+- **ohw** (16): Other Hardware
+- **ast** (17): Astronomy
+- **nbs** (18): Nearby Surface
+- **rmi** (19): Rocks (Misc)
+- **rtr** (20): Rover Tracks
+- **sky** (21): Sky
+- **rpa** (22): Rover Parts
+- **rlf** (23): Rock (Linear Features)
+- **sot** (24): Soil Trench
+## Statistics
+
+- **train**: 1762 images
+- **val**: 443 images
+- **test**: 739 images
+- **few_shot_train_10_shot**: 128 images
+- **few_shot_train_15_shot**: 175 images
+- **few_shot_train_1_shot**: 16 images
+- **few_shot_train_20_shot**: 220 images
+- **few_shot_train_2_shot**: 30 images
+- **few_shot_train_5_shot**: 67 images
+- **partition_0.01x_partition**: 19 images
+- **partition_0.02x_partition**: 33 images
+- **partition_0.05x_partition**: 81 images
+- **partition_0.10x_partition**: 184 images
+- **partition_0.20x_partition**: 361 images
+- **partition_0.25x_partition**: 447 images
+- **partition_0.50x_partition**: 878 images
+
+## Few-shot Splits
+
+This dataset includes the following few-shot training splits:
+
+- **few_shot_train_10_shot**: 128 images
+- **few_shot_train_15_shot**: 175 images
+- **few_shot_train_1_shot**: 16 images
+- **few_shot_train_20_shot**: 220 images
+- **few_shot_train_2_shot**: 30 images
+- **few_shot_train_5_shot**: 67 images
+
+Few-shot configurations:
+
+- **10_shot.csv**
+- **15_shot.csv**
+- **1_shot.csv**
+- **20_shot.csv**
+- **2_shot.csv**
+- **5_shot.csv**
+## Partition Splits
+
+This dataset includes the following partition splits:
+
+- **partition_0.01x_partition**: 19 images
+- **partition_0.02x_partition**: 33 images
+- **partition_0.05x_partition**: 81 images
+- **partition_0.10x_partition**: 184 images
+- **partition_0.20x_partition**: 361 images
+- **partition_0.25x_partition**: 447 images
+- **partition_0.50x_partition**: 878 images
+
+Partition configurations:
+
+- **0.01x_partition.csv**
+- **0.02x_partition.csv**
+- **0.05x_partition.csv**
+- **0.10x_partition.csv**
+- **0.20x_partition.csv**
+- **0.25x_partition.csv**
+- **0.50x_partition.csv**
+## Format
+
+Each example in the dataset has the following format:
+
+```
+{
+  'image': Image(...),  # PIL image
+  'labels': List[int],  # Multi-hot encoded binary vector (1 if class is present, 0 otherwise)
+  'feature_names': List[str],  # List of feature names (class short codes)
+}
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Mirali33/mb-surface_multi_label_cls")
+
+# Access an example
+example = dataset['train'][0]
+image = example['image']  # PIL image
+labels = example['labels']  # Multi-hot encoded binary vector
+
+# Example of how to find which classes are present in an image
+present_classes = [i for i, is_present in enumerate(labels) if is_present == 1]
+print(f"Classes present in this image: {present_classes}")
+```
+
+## Multi-label Classification
+
+In multi-label classification, each image can belong to multiple classes simultaneously. The labels are represented as a binary vector where a 1 indicates the presence of a class and a 0 indicates its absence.
+
+Unlike single-label classification where each image has exactly one class, multi-label classification allows modeling scenarios where multiple features can be present in the same image, which is often the case with Mars imagery.