mb-domars16k

A Mars image classification dataset for planetary science research.

Dataset Metadata

• License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)
• Version: 1.0
• Date Published: 2025-05-14
• Cite As: TBD

Classes

This dataset contains the following classes:

• 0: ael
• 1: rou
• 2: cli
• 3: aec
• 4: tex
• 5: smo
• 6: fss
• 7: rid
• 8: fse
• 9: sfe
• 10: fsf
• 11: fsg
• 12: sfx
• 13: cra
• 14: mix

Statistics

• train: 11305 images
• test: 1614 images
• val: 3231 images
• few_shot_train_2_shot: 30 images
• few_shot_train_1_shot: 15 images
• few_shot_train_10_shot: 150 images
• few_shot_train_5_shot: 75 images
• few_shot_train_15_shot: 225 images
• few_shot_train_20_shot: 300 images
• partition_train_0.01x_partition: 113 images
• partition_train_0.02x_partition: 226 images
• partition_train_0.50x_partition: 5652 images
• partition_train_0.20x_partition: 2261 images
• partition_train_0.05x_partition: 565 images
• partition_train_0.10x_partition: 1130 images
• partition_train_0.25x_partition: 2826 images

Few-shot Splits

This dataset includes the following few-shot training splits:

• few_shot_train_2_shot: 30 images
• few_shot_train_1_shot: 15 images
• few_shot_train_10_shot: 150 images
• few_shot_train_5_shot: 75 images
• few_shot_train_15_shot: 225 images
• few_shot_train_20_shot: 300 images

Few-shot configurations:

• 2_shot.csv
• 1_shot.csv
• 10_shot.csv
• 5_shot.csv
• 15_shot.csv
• 20_shot.csv

Partition Splits

This dataset includes the following training data partitions:

• partition_train_0.01x_partition: 113 images
• partition_train_0.02x_partition: 226 images
• partition_train_0.50x_partition: 5652 images
• partition_train_0.20x_partition: 2261 images
• partition_train_0.05x_partition: 565 images
• partition_train_0.10x_partition: 1130 images
• partition_train_0.25x_partition: 2826 images

Usage

from datasets import load_dataset

dataset = load_dataset("Mirali33/mb-domars16k")

Format

Each example in the dataset has the following format:

{
  'image': Image(...),  # PIL image
  'label': int,         # Class label
}