Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K - 100K
License:
File size: 3,644 Bytes
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---
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
}
```
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