macpaw-research/yolov11l-ui-elements-detection

🔍 YOLOv11l — UI Elements Detection

This model is a fine-tuned version of Ultralytics/YOLO11, trained to detect UI elements in macOS application screenshots.

It is part of the Screen2AX project — a research effort focused on generating accessibility metadata using computer vision.

---

🧠 Task Overview

• Task: Object Detection
• Target: Individual UI elements
• Supported Labels:

  ['AXButton', 'AXDisclosureTriangle', 'AXImage', 'AXLink', 'AXTextArea']
  

This model detects common interactive components typically surfaced in accessibility trees on macOS.

---

🗂 Dataset

• Training data: MacPaw/Screen2AX-Element

---

🚀 How to Use

🔧 Install Dependencies

pip install huggingface_hub ultralytics

🧪 Load the Model and Run Predictions

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

# Download the model
model_path = hf_hub_download(
    repo_id="macpaw-research/yolov11l-ui-elements-detection",
    filename="ui-elements-detection.pt",
)

# Load and run prediction
model = YOLO(model_path)
results = model.predict("/path/to/your/image")

# Display result
results[0].show()

---

📜 License

This model is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0), as inherited from the original YOLOv11 base model.

---

🔗 Related Projects

• Screen2AX Project
• Screen2AX HuggingFace Collection
• YOLOv11l — UI Groups Detection

---

✍️ Citation

If you use this model in your research, please cite the Screen2AX paper:

@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