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---
license: apache-2.0
dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
list:
list: int32
- name: category
list: string
splits:
- name: train
num_bytes: 121301432
num_examples: 678
- name: test
num_bytes: 26238185
num_examples: 158
- name: valid
num_bytes: 23829914
num_examples: 150
download_size: 119387398
dataset_size: 171369531
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
task_categories:
- object-detection
pretty_name: Screen2AX-Element
size_categories:
- n<1K
---
# 📦 Screen2AX-Element
Screen2AX-Element is part of the **Screen2AX** dataset suite, a research-driven collection for advancing accessibility in macOS applications using computer vision and deep learning.
This dataset focuses on **UI element detection** from macOS application screenshots and is designed for training and evaluating object detection models.
---
## 🧠 Dataset Summary
Each sample in the dataset consists of:
- An application **screenshot** (`image`)
- A dictionary of **UI elements** (`objects`) with 2 keys:
- `bbox` (`List[List[int]]`): List of bounding box coordinates in `[x_min, y_min, x_max, y_max]` format
- `category` (`List[str]`): List of labels indicating the UI element type (`AXButton`, `AXDisclosureTriangle`, `AXLink`, `AXTextArea`, `AXImage`)
The dataset supports training deep learning models for object detection tasks specifically tuned for graphical user interfaces (GUIs) on macOS.
**Splits:**
- `train`
- `valid`
- `test`
**Task Category:**
- `object-detection`
---
## 📚 Usage
### Load with `datasets` library
```python
from datasets import load_dataset
dataset = load_dataset("MacPaw/Screen2AX-Element")
```
### Example structure
```python
sample = dataset["train"][0]
print(sample.keys())
# dict_keys(['image', 'objects'])
print(sample["objects"])
# {'bbox': [[x_min, y_min, x_max, y_max], ...], 'category': ['AXButton', ...]}
```
---
## 📜 License
This dataset is licensed under the **Apache 2.0 License**.
---
## 🔗 Related Projects
- [Screen2AX Main Project Page](https://github.com/MacPaw/Screen2AX)
- [Screen2AX HuggingFace Collection](https://huggingface.co/collections/MacPaw/screen2ax-687dfe564d50f163020378b8)
---
## ✍️ Citation
If you use this dataset, please cite the Screen2AX paper:
```bibtex
@misc{muryn2025screen2axvisionbasedapproachautomatic,
title={Screen2AX: Vision-Based Approach for Automatic macOS Accessibility Generation},
author={Viktor Muryn and Marta Sumyk and Mariya Hirna and Sofiya Garkot and Maksym Shamrai},
year={2025},
eprint={2507.16704},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2507.16704},
}
```
---
## 🌐 MacPaw Research
Learn more at [https://research.macpaw.com](https://research.macpaw.com) |