README.md

---
language:
- en
license: apache-2.0
task_categories:
- image-text-to-text
- object-detection
task_ids:
- visual-question-answering
- instance-segmentation
tags:
- agent
- ui-automation
- screen-understanding
configs:
- config_name: macos
  data_files:
  - path: macos/data-00000-of-00001.arrow
    split: train
- config_name: windows
  data_files:
  - path: windows/data-00000-of-00001.arrow
    split: train
- config_name: linux-ubuntu
  data_files:
  - path: linux-ubuntu/data-00000-of-00001.arrow
    split: train
- config_name: linux-mint
  data_files:
  - path: linux-mint/data-00000-of-00001.arrow
    split: train
---
# Screen2Coord_denorm_extend Dataset































**Screen2Coord** is a dataset for training models that take a **screenshot, screen dimensions, and a textual action description** as input and output the **coordinates of the target bounding box** on the screen. This dataset is intended for image-text-to-text LLMs applied to user interface interactions.































## Dataset Structure









### New feature! Windows, MacOS, Linux-Ubuntu subsets!











### Data Instances































A typical data instance in Screen2Coord consists of:































- `image`: A screenshot image in PNG format
- `mapped_denorm_bboxes`: List of bounding box objects containing:
  - `bbox`: List of integers `[x, y, width, height]` specifying the bounding box coordinates in denormalized 0-1000 system
  - `texts`: List of textual descriptions associated with the bounding box (e.g., `"click on my profile"`)
































### Data Fields































- `image`: Image file in PNG format
- `mapped_denorm_bboxes`: Sequence of dictionaries with bounding box information (coordinates in denormalized 0-1000 system)
































### Data Splits

The dataset contains the following splits:

- `macos` (train): 174 examples
- `windows` (train): 166 examples
- `linux-ubuntu` (train): 1 examples
- `linux-mint` (train): 1 examples
## Purpose / How to Use































The main idea of this dataset is to train **image-text-to-text LLMs** that can interpret a screenshot and textual prompt **along with screen dimensions and instructions**, e.g., `"open the browser"`.































The model receives:































- **Screenshot of the screen**















- **Screen size** `[width, height]`















- **Textual instruction** (prompt)































And outputs:































- **Bounding box coordinates** corresponding to where the action should be performed.















  For example, clicking in the middle of the predicted bounding box executes the instructed action.































This enables models to perform **UI actions** based on visual context and natural language instructions.































For example, during training, you can provide the model with a full prompt from an agent system and also add a click tool, supplying the labeled bounding boxes from this dataset in the tool call.































## Contributions































If you can help with **annotations** or support the dataset **financially**, please send a direct message. The dataset is updated in my spare time.