mlfoundations-cua-dev/easyr1-10k-hard-qwen7b-easy-gta1-4MP-professional-apps-grounding-only-no-resolution-in-prompt

easyr1-10k-hard-qwen7b-easy-gta1-4MP-professional-apps-grounding-only-no-resolution-in-prompt

This dataset was generated using the EasyR1 grounding dataset pipeline.

Generation Details

• —Generated on: 2025-08-26 12:16:32 UTC
• —Script: push_easyr1_to_hf.py
• —Data directory: /lustre/fsw/portfolios/nvr/users/aawadalla/LLaMA-Factory/data

Parameters Used

• —Maximum samples: 10000
• —Image resize (max megapixels): 4.0 MP
• —Minimum native image resolution: 0.0 MP
• —Prompt format: gta1
• —Output format: coordinates
• —Random seed: 42
• —Resampling enabled: False
• —Icon upsampling ratio: Disabled (random sampling)
• —pc-agent-e deduplication: False
• —Debug images enabled: True (annotated images with red bounding boxes included)

Dataset Groups

The following JSON/JSONL files were used to create this dataset:

Dataset Group 1

• —grounding-data-filters/professional-apps-grounding-filtered-gta1-7b-converted-notgrounded-qwen25vl7b-QwenQwen2.5-VL-7B-Instruct-qwentoolcall.jsonl

Dataset Statistics

• —Total training samples: 3182
• —Image dimensions: Variable
• —Columns: imagepath, prompt, normalizedbbox, images, easyr1prompt, bbox, messages, originalimagewidth, imagewidth, imageheight, annotatedimages

System Prompt

The following system prompt is used for this dataset:

You are an expert UI element locator. Given a GUI image and a user's element description, provide the coordinates of the specified element as a single (x,y) point. For elements with area, return the center point.

Output the coordinate pair exactly:
(x,y)

Sample Entry

• —User prompt: <image> Close the Graph1 window
• —Assistant response: (1521,198)
• —Bounding box: [1505, 182, 1537, 215]
• —Image path: professional-apps-grounding-images/profapps0000142frame001521.jpg

Usage

from datasets import load_dataset

dataset = load_dataset("mlfoundations-cua-dev/easyr1-10k-hard-qwen7b-easy-gta1-4MP-professional-apps-grounding-only-no-resolution-in-prompt")

# Access the training data
train_data = dataset['train']

# Example: Get the first sample
sample = train_data[0]
images = sample['images']
messages = sample['messages']
bbox = sample['bbox']
annotated_images = sample['annotated_images']  # Available when debug images are enabled```

## Prompt Formats

### gta1

Standard GTA1 format without resolution information. Outputs coordinates in (x,y) format.

## License

Please refer to the original dataset licenses for usage restrictions.