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Mirali33/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/ #… See the full description on the dataset page: https://huggingface.co/datasets/Mirali33/mb-crater_binary_seg.

sourceHugging Facecc-by-4.0updated 11mo agoView on Hugging Face
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mb-craterbinaryseg

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
  • partitiontrain0.01x_partition: 36 images
  • partitiontrain0.02x_partition: 72 images
  • partitiontrain0.50x_partition: 1800 images
  • partitiontrain0.20x_partition: 720 images
  • partitiontrain0.05x_partition: 180 images
  • partitiontrain0.10x_partition: 360 images
  • partitiontrain0.25x_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
}