Datasets:

Mirali33
/

mb-surface_cls

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - image-classification
task_ids:
  - multi-class-image-classification
pretty_name: mb-surface_cls
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': apx
            '1': act
            '2': arm
            '3': art
            '4': cct
            '5': cio
            '6': clr
            '7': dls
            '8': dri
            '9': drh
            '10': drp
            '11': drt
            '12': flr
            '13': gro
            '14': hor
            '15': inl
            '16': lar
            '17': ltv
            '18': mah
            '19': mct
            '20': mas
            '21': mca
            '22': nsk
            '23': obt
            '24': pbo
            '25': ptu
            '26': pto
            '27': rem
            '28': rrd
            '29': san
            '30': sco
            '31': sun
            '32': tur
            '33': whe
            '34': whj
            '35': wht

mb-surface_cls

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: apx
1: act
2: arm
3: art
4: cct
5: cio
6: clr
7: dls
8: dri
9: drh
10: drp
11: drt
12: flr
13: gro
14: hor
15: inl
16: lar
17: ltv
18: mah
19: mct
20: mas
21: mca
22: nsk
23: obt
24: pbo
25: ptu
26: pto
27: rem
28: rrd
29: san
30: sco
31: sun
32: tur
33: whe
34: whj
35: wht

Statistics

train: 6580 images
test: 1594 images
val: 1293 images
few_shot_train_2_shot: 72 images
few_shot_train_1_shot: 36 images
few_shot_train_10_shot: 355 images
few_shot_train_5_shot: 180 images
few_shot_train_15_shot: 522 images
few_shot_train_20_shot: 673 images
partition_train_0.01x_partition: 66 images
partition_train_0.02x_partition: 132 images
partition_train_0.50x_partition: 3086 images
partition_train_0.20x_partition: 1316 images
partition_train_0.05x_partition: 330 images
partition_train_0.10x_partition: 661 images
partition_train_0.25x_partition: 1617 images

Few-shot Splits

This dataset includes the following few-shot training splits:

few_shot_train_2_shot: 72 images
few_shot_train_1_shot: 36 images
few_shot_train_10_shot: 355 images
few_shot_train_5_shot: 180 images
few_shot_train_15_shot: 522 images
few_shot_train_20_shot: 673 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: 66 images
partition_train_0.02x_partition: 132 images
partition_train_0.50x_partition: 3086 images
partition_train_0.20x_partition: 1316 images
partition_train_0.05x_partition: 330 images
partition_train_0.10x_partition: 661 images
partition_train_0.25x_partition: 1617 images

Usage

from datasets import load_dataset

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

Format

Each example in the dataset has the following format:

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