OS-Copilot

OS-Atlas: A Foundation Action Model For Generalist GUI Agents [\[🏠Homepage\]](https://osatlas.github.io) [\[💻Code\]](https://github.com/OS-Copilot/OS-Atlas) [\[🚀Quick Start\]](#quick-start) [\[📝Paper\]](https://arxiv.org/abs/2410.23218) [\[🤗Models\]](https://huggingface.co/collections/OS-Copilot/os-atlas-67246e44003a1dfcc5d0d045)[\[🤗Data\]](https://huggingface.co/datasets/OS-Copilot/OS-Atlas-data) [\[🤗ScreenSpot-v2\]](https://huggingface.co/datasets/OS-Copilot/ScreenSpot-v2) OS-Atlas provides a series of models specifically designed for GUI agents. For GUI grounding tasks, you can use: - OS-Atlas-Base-7B - OS-Atlas-Base-4B For generating single-step actions in GUI agent tasks, you can use: - OS-Atlas-Pro-7B - OS-Atlas-Pro-4B Quick Start OS-Atlas-Base-7B is a GUI grounding model finetuned from Qwen2-VL-7B-Instruct. Notes: Our models accept images of any size as input. The model outputs are normalized to relative coordinates within a 0-1000 range (either a center point or a bounding box defined by top-left and bottom-right coordinates). For visualization, please remember to convert these relative coordinates back to the original image dimensions. Inference Example First, ensure that the necessary dependencies are installed: Then download the example image and save it to the current directory. Citation If you find this repository helpful, feel free to cite our paper:

OS-Atlas: A Foundation Action Model For Generalist GUI Agents [\[🏠Homepage\]](https://osatlas.github.io) [\[💻Code\]](https://github.com/OS-Copilot/OS-Atlas) [\[🚀Quick Start\]](#quick-start) [\[📝Paper\]](https://arxiv.org/abs/2410.23218) [\[🤗Models\]](https://huggingface.co/collections/OS-Copilot/os-atlas-67246e44003a1dfcc5d0d045)[\[🤗Data\]](https://huggingface.co/datasets/OS-Copilot/OS-Atlas-data) [\[🤗ScreenSpot-v2\]](https://huggingface.co/datasets/OS-Copilot/ScreenSpot-v2) OS-Atlas provides a series of models specifically designed for GUI agents. For GUI grounding tasks, you can use: - OS-Atlas-Base-7B - OS-Atlas-Base-4B For generating single-step actions in GUI agent tasks, you can use: - OS-Atlas-Pro-7B - OS-Atlas-Pro-4B Quick Start OS-Atlas-Base-4B is a GUI grounding model finetuned from InternVL2-4B. Notes: Our models accept images of any size as input. The model outputs are normalized to relative coordinates within a 0-1000 range (either a center point or a bounding box defined by top-left and bottom-right coordinates). For visualization, please remember to convert these relative coordinates back to the original image dimensions. Inference Example First, install the `transformers` library: For additional dependencies, please refer to the InternVL2 documentation Then download the example image and save it to the current directory. Citation If you find this repository helpful, feel free to cite our paper:

OS-Atlas: A Foundation Action Model For Generalist GUI Agents [\[🏠Homepage\]](https://osatlas.github.io) [\[💻Code\]](https://github.com/OS-Copilot/OS-Atlas) [\[🚀Quick Start\]](#quick-start) [\[📝Paper\]](https://arxiv.org/abs/2410.23218) [\[🤗Models\]](https://huggingface.co/collections/OS-Copilot/os-atlas-67246e44003a1dfcc5d0d045)[\[🤗Data\]](https://huggingface.co/datasets/OS-Copilot/OS-Atlas-data) [\[🤗ScreenSpot-v2\]](https://huggingface.co/datasets/OS-Copilot/ScreenSpot-v2) OS-Atlas provides a series of models specifically designed for GUI agents. For GUI grounding tasks, you can use: - OS-Atlas-Base-7B - OS-Atlas-Base-4B For generating single-step actions in GUI agent tasks, you can use: - OS-Atlas-Pro-7B - OS-Atlas-Pro-4B `OS-Atlas-Pro-4B` is a GUI action model finetuned from OS-Atlas-Base-4B. By taking as input a system prompt, basic and custom actions, and a task instruction, the model generates thoughtful reasoning (`thought`) and executes the appropriate next step (`action`). Note that the released `OS-Atlas-Pro-4B` model is described in the Section 5.4 of the paper. Compared to the OS-Atlas model in Tables 4 and 5, the Pro model demonstrates superior generalizability and performance. Critically, it is not constrained to specific tasks or training datasets merely to satisfy particular experimental conditions like OOD and SFT. Furthermore, this approach prevents us from overdosing HuggingFace by uploading over 20+ distinct model checkpoints. Installation To use `OS-Atlas-Pro-4B`, first install the necessary dependencies: For additional dependencies, please refer to the InternVL2 documentation Example Inference Code First download the example image and save it to the current directory. Below is an example of how to perform inference using the model: Citation If you find this repository helpful, feel free to cite our paper: