Datasets:
Tasks:
Image Classification
Formats:
parquet
Sub-tasks:
multi-label-image-classification
Languages:
English
Size:
1K - 10K
License:
File size: 2,853 Bytes
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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: mars-multi-label-classification
---
# mars-multi-label-classification
A Mars image multi-label classification dataset for planetary science research.
## Dataset Metadata
* **License:** CC-BY-4.0 (Creative Commons Attribution 4.0 International)
* **Version:** 1.0
* **Date Published:** 2025-05-10
* **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
## 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("gremlin97/mars-multi-label-classification")
# 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.
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