Datasets:
Tasks:
Image Segmentation
Formats:
parquet
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
10K - 100K
License:
File size: 1,957 Bytes
da4573f b71ee81 da4573f b71ee81 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
---
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-s5mars
---
# 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:** 2025-05-15
* **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_0.01x_partition: 49 images
- partition_train_0.02x_partition: 99 images
- partition_train_0.50x_partition: 2498 images
- partition_train_0.20x_partition: 999 images
- partition_train_0.05x_partition: 249 images
- partition_train_0.10x_partition: 499 images
- partition_train_0.25x_partition: 1249 images
## Usage
```python
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
}
```
|