Duplicate from Mirali33/mb-surface_multi_label_cls

Co-authored-by: Mirali Purohit <[email protected]>

.gitattributes

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+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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README.md

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+---
+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:** 2025-10-23
+* **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
+  'label': List[int],  # Multi-hot encoded binary vector (1 if class is present, 0 otherwise)
+  'feature_name': 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
+label = example['label']  # 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(label) 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.

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mapping.json

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+{
+    "0": [
+        "rah",
+        "Rock Abrasion Tool (RAT) Hole"
+    ],
+    "1": [
+        "cla",
+        "Clasts"
+    ],
+    "2": [
+        "dur",
+        "Dunes/Ripples"
+    ],
+    "3": [
+        "soi",
+        "Soil"
+    ],
+    "4": [
+        "roc",
+        "Rock Outcrops"
+    ],
+    "5": [
+        "clr",
+        "Close-up Rock"
+    ],
+    "6": [
+        "rab",
+        "Rock Abrasion Tool (RAT) Brushed Target"
+    ],
+    "7": [
+        "div",
+        "Distant Vista"
+    ],
+    "8": [
+        "rod",
+        "Rover Deck"
+    ],
+    "9": [
+        "bso",
+        "Bright Soil"
+    ],
+    "10": [
+        "flr",
+        "Float Rocks"
+    ],
+    "11": [
+        "art",
+        "Artifacts"
+    ],
+    "12": [
+        "pct",
+        "Pancam Calibration Target"
+    ],
+    "13": [
+        "arh",
+        "Arm Hardware"
+    ],
+    "14": [
+        "rrf",
+        "Rock (Round Features)"
+    ],
+    "15": [
+        "sph",
+        "Spherules"
+    ],
+    "16": [
+        "ohw",
+        "Other Hardware"
+    ],
+    "17": [
+        "ast",
+        "Astronomy"
+    ],
+    "18": [
+        "nbs",
+        "Nearby Surface"
+    ],
+    "19": [
+        "rmi",
+        "Rocks (Misc)"
+    ],
+    "20": [
+        "rtr",
+        "Rover Tracks"
+    ],
+    "21": [
+        "sky",
+        "Sky"
+    ],
+    "22": [
+        "rpa",
+        "Rover Parts"
+    ],
+    "23": [
+        "rlf",
+        "Rock (Linear Features)"
+    ],
+    "24": [
+        "sot",
+        "Soil Trench"
+    ]
+}