Datasets:
Tasks:
Image Segmentation
Formats:
parquet
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
1K - 10K
License:
File size: 1,909 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-segmentation
task_ids:
- semantic-segmentation
pretty_name: mb-crater_binary_seg
---
# mb-crater_binary_seg
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:** 2025-05-15
* **Cite As:** TBD
## Classes
This dataset contains the following classes:
- 0: Background
- 1: Crater
## 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: 3600 images
- val: 900 images
- test: 900 images
- partition_train_0.01x_partition: 36 images
- partition_train_0.02x_partition: 72 images
- partition_train_0.50x_partition: 1800 images
- partition_train_0.20x_partition: 720 images
- partition_train_0.05x_partition: 180 images
- partition_train_0.10x_partition: 360 images
- partition_train_0.25x_partition: 900 images
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("Mirali33/mb-crater_binary_seg")
```
## 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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