mlfoundations-cua-dev/easyr1-10k-hard-qwen7b-easy-gta1-4MP-no-pixmo-points

easyr1-10k-hard-qwen7b-easy-gta1-4MP-no-pixmo-points

This dataset was generated using the EasyR1 grounding dataset pipeline.

Generation Details

• —Generated on: 2025-08-23 16:05:22 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_with_resolution
• —Output format: coordinates
• —Random seed: 42
• —Resampling enabled: False
• —Icon upsampling ratio: Disabled (random sampling)

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

Dataset Group 3

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 4

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 5

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 6

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 7

Files (intersection of kept samples across all files):

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

Dataset Group 8

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 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> In the image above, I will give a series of descriptions of the element to be clicked. Please predict where you want to click (with point). This element serves as a gateway to a login system, enabling users to access their accounts on the platform.
• —Assistant response: (536,7)
• —Bounding box: [523, 0, 550, 15]
• —Image path: autogui-images/autogui1490590606e04ad.png

Usage

from datasets import load_dataset

dataset = load_dataset("mlfoundations-cua-dev/easyr1-10k-hard-qwen7b-easy-gta1-4MP-no-pixmo-points")

# 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.