Datasets:
Tasks:
Image Segmentation
Formats:
parquet
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
10K - 100K
License:
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mb-s5mars
A segmentation dataset for planetary science applications.
Dataset Metadata
- License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)
- Version: 1.0
- Date Published: 2025-10-23
- Cite As: TBD
Classes
This dataset contains the following classes:
- 0: Background
- 1: Bedrock
- 2: Hole
- 3: Ridge
- 4: Rock
- 5: Rover
- 6: Sand / Soil
- 7: Sky
- 8: Track
Directory Structure
The dataset follows this structure:
dataset/
├── train/
│ ├── images/ # Image files
│ └── masks/ # Segmentation masks
├── val/
│ ├── images/ # Image files
│ └── masks/ # Segmentation masks
├── test/
│ ├── images/ # Image files
│ └── masks/ # Segmentation masks
Statistics
- train: 4997 images
- val: 200 images
- test: 800 images
- partition_train_0.02x_partition: 99 images
- partition_train_0.50x_partition: 2498 images
- partition_train_0.10x_partition: 499 images
- partition_train_0.25x_partition: 1249 images
- partition_train_0.05x_partition: 249 images
- partition_train_0.01x_partition: 49 images
- partition_train_0.20x_partition: 999 images
Usage
from datasets import load_dataset
dataset = load_dataset("Mirali33/mb-s5mars")
Format
Each example in the dataset has the following format:
{
'image': Image(...), # PIL image
'mask': Image(...), # PIL image of the segmentation mask
'width': int, # Width of the image
'height': int, # Height of the image
'class_labels': [str,...] # List of class names present in the mask
}
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