mlfoundations-cua-dev/easyr1-10k-hard-qwen7b-easy-gta1-4MP-in-house-pro-apps-remove-worst-data-sources

easyr1-10k-hard-qwen7b-easy-gta1-4MP-in-house-pro-apps-remove-worst-data-sources

This dataset was generated using the EasyR1 grounding dataset pipeline.

Generation Details

• —Generated on: 2025-08-22 17:10:38 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
• —Prompt format: gta1_with_resolution
• —Output format: coordinates
• —Random seed: 42
• —Resampling enabled: False

Dataset Groups

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

Dataset Group 1

Files (intersection of kept samples across all files):

• —grounding-data-filters/autogui-grounding-only-qwentoolcall-notgrounded-QwenQwen2.5-VL-7B-Instruct-qwentoolcall.jsonl
• —grounding-data-filters/autogui-grounding-only-gta1-correctlygrounded-HelloKKMeGTA1-7B-gta1.jsonl

Dataset Group 2

Files (intersection of kept samples across all files):

• —grounding-data-filters/pc-e-grounding-only-claude-instructions-qwentoolcall-notgrounded-QwenQwen2.5-VL-7B-Instruct-qwentoolcall.jsonl
• —grounding-data-filters/pc-e-grounding-only-claude-instructions-gta1-correctlygrounded-HelloKKMeGTA1-7B-gta1.jsonl

Dataset Group 3

Files (intersection of kept samples across all files):

• —grounding-data-filters/omniact-grounding-only-qwentoolcall-notgrounded-QwenQwen2.5-VL-7B-Instruct-qwentoolcall.jsonl
• —grounding-data-filters/omniact-grounding-only-gta1-correctlygrounded-HelloKKMeGTA1-7B-gta1.jsonl

Dataset Group 4

Files (intersection of kept samples across all files):

• —grounding-data-filters/showui-desktop-qwentoolcall-notgrounded-QwenQwen2.5-VL-7B-Instruct-qwentoolcall.jsonl
• —grounding-data-filters/showui-desktop-gta1-correctlygrounded-HelloKKMeGTA1-7B-gta1.jsonl

Dataset Group 5

Files (intersection of kept samples across all files):

• —grounding-data-filters/showui-web-qwentoolcall-notgrounded-QwenQwen2.5-VL-7B-Instruct-qwentoolcall.jsonl
• —grounding-data-filters/showui-web-gta1-correctlygrounded-HelloKKMeGTA1-7B-gta1.jsonl

Dataset Group 6

Files (intersection of kept samples across all files):

• —grounding-data-filters/waveui-qwentoolcall-notgrounded-QwenQwen2.5-VL-7B-Instruct-qwentoolcall.jsonl
• —grounding-data-filters/waveui-gta1-correctlygrounded-HelloKKMeGTA1-7B-gta1.jsonl

Dataset Group 7

• —grounding-data-filters/professional-apps-grounding-icon-only-filtered-gta1-7b-converted.json

Dataset Group 8

• —grounding-data-filters/professional-apps-grounding-text-only-filtered-gta1-7b-converted.json

Dataset Statistics

• —Total training samples: 10000
• —Image dimensions: Variable
• —Columns: imagepath, prompt, normalizedbbox, images, easyr1prompt, bbox, messages, imagewidth, image_height

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. The image resolution is height 2048 and width 2048. For elements with area, return the center point.

Output the coordinate pair exactly:
(x,y)

Sample Entry

• —User prompt: <image> Choose Overview
• —Assistant response: (1308,176)
• —Bounding box: [1242, 153, 1375, 199]
• —Image path: showui-web-images/showuiweb017697.jpg

Usage

from datasets import load_dataset

dataset = load_dataset("mlfoundations-cua-dev/easyr1-10k-hard-qwen7b-easy-gta1-4MP-in-house-pro-apps-remove-worst-data-sources")

# 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']

Prompt Formats

gta1withresolution

GTA1 format with image resolution included in the system prompt. Outputs coordinates in (x,y) format.

License

Please refer to the original dataset licenses for usage restrictions.