README.md

---
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

```python
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
}
```