Datasets:
Tasks:
Image Segmentation
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
1K<n<10K
License:
File size: 1,814 Bytes
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---
annotations_creators:
- human
language:
- en
license: mit
pretty_name: VisionReasoner UI Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
---
# VisionReasoner UI Dataset
This dataset contains user interface (UI) images along with associated annotation prompts and solutions for fine-tuning the VisionReasoner model.
## Dataset Description
- **Size**: 245 samples
- **Task**: Image Segmentation (Semantic Segmentation)
- **Language**: English
- **License**: MIT
## Structure
- `images/`: Folder containing UI images (`.webp` format)
- `visionreasoner_dataset.parquet`: Metadata file containing:
- `id`: Unique identifier for each sample
- `problem`: Annotation prompt describing the UI element to segment
- `solution`: JSON-formatted segmentation solution with bounding boxes and points
- `image`: Reference to the image file
- `img_height`: Image height in pixels
- `img_width`: Image width in pixels
## Usage
To load and preview the dataset:
```python
from datasets import load_dataset
# Load the dataset with trust_remote_code=True
dataset = load_dataset("shirve13/Demo", trust_remote_code=True)
print(dataset["train"][0])
```
**Note**: Make sure to use `trust_remote_code=True` as this dataset uses a custom loading script.
## Dataset Loading Script
The dataset uses a custom loading script (`demo.py`) that:
- Loads metadata from the parquet file
- Handles image paths correctly
- Provides proper dataset features for Hugging Face compatibility
## Citation
If you use this dataset in your research, please cite:
```bibtex
@dataset{visionreasoner_ui_dataset,
title={VisionReasoner UI Dataset},
author={shirve13},
year={2024},
url={https://huggingface.co/datasets/shirve13/Demo}
}
```
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