--- 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)