metadata
task_categories:
- image-segmentation
tags:
- roboflow
- roboflow2huggingface
Dataset Labels
['lion']
Number of Images
{'valid': 30, 'train': 345}
How to Use
- Install datasets:
pip install datasets
- Load the dataset:
from datasets import load_dataset
ds = load_dataset("Textovic/lion-mask2former", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/myworkspace-skwwo/lionssegmentation/dataset/8
Citation
@misc{
lionssegmentation_dataset,
title = { LionsSegmentation Dataset },
type = { Open Source Dataset },
author = { Myworkspace },
howpublished = { \\url{ https://universe.roboflow.com/myworkspace-skwwo/lionssegmentation } },
url = { https://universe.roboflow.com/myworkspace-skwwo/lionssegmentation },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { jul },
note = { visited on 2025-07-22 },
}
License
CC BY 4.0
Dataset Summary
This dataset was exported via roboflow.com on July 22, 2025 at 5:24 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
- collaborate with your team on computer vision projects
- collect & organize images
- understand and search unstructured image data
- annotate, and create datasets
- export, train, and deploy computer vision models
- use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 375 images. Lion are annotated in COCO format.
The following pre-processing was applied to each image:
- Auto-orientation of pixel data (with EXIF-orientation stripping)
- Resize to 640x640 (Stretch)
The following augmentation was applied to create 3 versions of each source image:
- Random rotation of between -18 and +18 degrees
- Random shear of between -10° to +10° horizontally and -10° to +10° vertically
- Salt and pepper noise was applied to 2.44 percent of pixels