metadata

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: 2025-05-10
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
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 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

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

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.