
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding
Training data is available at the following link: data. We will gradually upload data for all apps.
Training json files are available at the following link: training_jsons.
To start collecting data, run the script main/corpus/googleCreatDataset/arm_graph_para_lock.py.
Example usage:
python googleCreatDataset/arm_graph_para_lock.py --device_name 10.53.89.79:6532 --systemPort 8112 --appid 8201 --command_executorhttp://127.0.0.1:4812/wd/hub--appPackage com.lucky.luckyclient --name_en lucky --diff_max 0.5 --diff_png 0.3 --waitadb 8 --prefix lucky0_3_1_2_ --recheck -1
Running the above collection instruction requires the following additional installations.
Install Node.js and Appium:
curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash -
sudo apt-get install -y nodejs
sudo apt install npm
npm install -g appium@1.18.3
Install graphical libraries:
sudo apt-get install xorg
Activate the Python virtual environment:
source /path/to/new/virtual/environment/bin/activate
Install Appium Python Client 1.3.0:
pip install Appium-Python-Client==1.3.0
-1 for no recheck.
The code for generating data for each task can be found in the following directories:
Our test data is available at data.
The dataset of this project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
The source code of the this is licensed under the Apache 2.0 license.
If you'd like to use our benchmark or cite this paper, please kindly use the reference below:
@article{wu2024mobilevlm,
title={Mobilevlm: A vision-language model for better intra-and inter-ui understanding},
author={Wu, Qinzhuo and Xu, Weikai and Liu, Wei and Tan, Tao and Liu, Jianfeng and Li, Ang and Luan, Jian and Wang, Bin and Shang, Shuo},
journal={arXiv preprint arXiv:2409.14818},
year={2024}
}
5 commits
Python
99.3%

MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding
Training data is available at the following link: data. We will gradually upload data for all apps.
Training json files are available at the following link: training_jsons.
To start collecting data, run the script main/corpus/googleCreatDataset/arm_graph_para_lock.py.
Example usage:
python googleCreatDataset/arm_graph_para_lock.py --device_name 10.53.89.79:6532 --systemPort 8112 --appid 8201 --command_executorhttp://127.0.0.1:4812/wd/hub--appPackage com.lucky.luckyclient --name_en lucky --diff_max 0.5 --diff_png 0.3 --waitadb 8 --prefix lucky0_3_1_2_ --recheck -1
Running the above collection instruction requires the following additional installations.
Install Node.js and Appium:
curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash -
sudo apt-get install -y nodejs
sudo apt install npm
npm install -g appium@1.18.3
Install graphical libraries:
sudo apt-get install xorg
Activate the Python virtual environment:
source /path/to/new/virtual/environment/bin/activate
Install Appium Python Client 1.3.0:
pip install Appium-Python-Client==1.3.0
-1 for no recheck.
The code for generating data for each task can be found in the following directories:
Our test data is available at data.
The dataset of this project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
The source code of the this is licensed under the Apache 2.0 license.
If you'd like to use our benchmark or cite this paper, please kindly use the reference below:
@article{wu2024mobilevlm,
title={Mobilevlm: A vision-language model for better intra-and inter-ui understanding},
author={Wu, Qinzhuo and Xu, Weikai and Liu, Wei and Tan, Tao and Liu, Jianfeng and Li, Ang and Luan, Jian and Wang, Bin and Shang, Shuo},
journal={arXiv preprint arXiv:2409.14818},
year={2024}
}
5 commits
Python
99.3%