---
license: cc-by-4.0
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- image-text-to-text
tags:
- Tecky
- UI
- VLM
---
# Tecky UI Automation Dataset

## 🧠 Tecky: Your True Virtual Employee

**Tecky** is an AI-powered virtual teammate that lives on your machine. It learns how you interact with apps and automates workflows across both public tools and internal software.

Tecky acts as a **context-aware agent**, observing how users complete digital tasks and suggesting or executing them through a human-like interface.

---

## 📊 Dataset Overview


This dataset is the foundation for training **Vision-Language Models (VLMs)** to understand **graphical user interfaces (GUIs)** in desktop environments. It contains thousands of labeled UI elements from real-world app screenshots.

> This dataset is focused solely on **UI element detection and understanding**.  
> Workflow modeling and full action-sequence understanding will be released in a future dataset.

With this data, models can:

- Parse GUI screenshots
- Identify actionable elements like buttons, inputs, sliders
- Predict appropriate click targets based on instructions
- Learn general UI semantics across diverse applications

---

## 🎯 Goal

The main objective is to train VLMs that:

- Understand visual structure in desktop environments
- Predict user actions (like click targets) based on screenshots and instructions
- Learn generic UI semantics across apps and layouts
- Enable automation agents like Tecky to reason and act visually

The dataset is a unified and normalized collection from open sources, focused on real desktop UI.

---

## 📁 Dataset Structure

Each entry in the JSONL files includes:

- `system` prompt (task context for Tecky)
- `user` message with:
  - `image`: path to a screenshot
  - `text`: instruction (e.g. “click the settings icon”)
- `assistant` response:
  - JSON object with:
    - `actions`: array of predicted clicks
    - `status_update`: short explanation of the action

**Image paths** are relative to the repo and organized in folders like `images_split/00000-09999/`.

We provide:
- `train.jsonl`: main training set
- `valid.jsonl`: 5% validation sample from across all sources

---

## ⚖️ Licenses & Attribution

This dataset merges multiple public sources. Each is preserved under its original license and properly credited.

| Dataset | License | Attribution |
|--------|---------|-------------|
| **UI element Detect Computer Vision Project** by Uled | CC BY 4.0 | `@misc{ ui-element-detect_dataset, title = { UI element Detect Dataset }, type = { Open Source Dataset }, author = { UIed }, howpublished = { \url{ https://universe.roboflow.com/uied/ui-element-detect } }, url = { https://universe.roboflow.com/uied/ui-element-detect }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { oct }, note = { visited on 2025-07-11 }, } ` |
| **UI-Elements-Detection-Dataset** by YashJain | Apache 2.0 | [YashJain/UI-Elements-Detection-Dataset](https://huggingface.co/datasets/YashJain/UI-Elements-Detection-Dataset) |
| **ui_elemenz_dataset** by Maleke Chaker | MIT | [Original repo](https://www.kaggle.com/datasets/malekechaker/ui-elemenz-dataset) |
| **ScreenSpot** by RootsAutomation | Apache 2.0 | [rootsautomation/ScreenSpot](https://huggingface.co/datasets/rootsautomation/ScreenSpot) |
| **Annotated UI Element Dataset for Desktop Environments** | CC BY 4.0 | Martínez-Rojas, A., Rodríguez-Ruíz, A., González Enríquez, J., & Jiménez-Ramírez, A. (2024). Annotated UI Element Dataset for Desktop Environments [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.10822752](https://doi.org/10.5281/zenodo.10822752) |

All usage of this dataset must respect these licenses. This release falls under **CC BY 4.0** to ensure attribution to all original sources.

---

## 🧠 Intended Use

This dataset is intended for:

- Training VLMs and UI agents (like Tecky)
- Research on UI interaction understanding
- Fine-tuning language models to act visually
- Prototyping autonomous UX testing and interface parsing

Not intended for facial recognition, surveillance, or non-UI classification tasks.

---

## 🙌 Contributing

Contributions welcome!

- Fork the dataset repo
- Add your JSONL/image data
- Submit a pull request
- Or open an issue with feedback or questions

---

## 🧾 Citation

If you use this dataset, please cite the original sources listed above and acknowledge the **Tecky** Project.