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-segmentation
task_ids:
- semantic-segmentation
pretty_name: mb-mmls
---

# mb-mmls

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: Landslide

## 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: 275 images
- val: 31 images
- test: 256 images
- partition_train_0.02x_partition: 5 images
- partition_train_0.50x_partition: 137 images
- partition_train_0.10x_partition: 27 images
- partition_train_0.25x_partition: 68 images
- partition_train_0.05x_partition: 13 images
- partition_train_0.01x_partition: 2 images
- partition_train_0.20x_partition: 55 images

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("Mirali33/mb-mmls")
```

## 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
}
```