Update README.md

README.md

@@ -14,22 +14,16 @@ pipeline_tag: image-text-to-text
 </div>
 
 ## Overview
-![os-atlas](https://github.com/user-attachments/assets/cf2ee020-5e15-4087-9a7e-75cc43662494)
+![os-genesis](https://cdn-uploads.huggingface.co/production/uploads/6064a0eeb1703ddba0d458b9/XvcAh92uvJQglmIu_L_nK.png)
 
-OS-Genesis is a manual-free data pipeline for synthesizing GUI agent trajectories.
+We introduce OS-Genesis, an interaction-driven pipeline that synthesizes high-quality and diverse GUI agent trajectory data without human supervision. By leveraging reverse task synthesis, OS-Genesis enables effective training of GUI agents to achieve superior performance on dynamic benchmarks such as AndroidWorld and WebArena.
 
-For GUI grounding tasks, you can use:
-- [OS-Atlas-Base-7B](https://huggingface.co/OS-Copilot/OS-Atlas-Base-7B)
-- [OS-Atlas-Base-4B](https://huggingface.co/OS-Copilot/OS-Atlas-Base-4B)
+## Quick Start
+OS-Genesis-8B-AC is a mobile action model finetuned from [InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B).
 
-For generating single-step actions in GUI agent tasks, you can use:
-- [OS-Atlas-Pro-7B](https://huggingface.co/OS-Copilot/OS-Atlas-Pro-7B)
-- [OS-Atlas-Pro-4B](https://huggingface.co/OS-Copilot/OS-Atlas-Pro-4B)
+###  Model Zoo
 
-## Quick Start
-OS-Atlas-Base-4B is a GUI grounding model finetuned from [InternVL2-4B](https://huggingface.co/OpenGVLab/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:
@@ -135,21 +129,19 @@ tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast
 pixel_values = load_image('./web_dfacd48d-d2c2-492f-b94c-41e6a34ea99f.png', max_num=6).to(torch.bfloat16).cuda()
 generation_config = dict(max_new_tokens=1024, do_sample=True)
 
-question = "In the screenshot of this web page, please give me the coordinates of the element I want to click on according to my instructions(with point).\n\"'Champions League' link\""
+question = "<image> You are a GUI task expert, I will provide you with a high-level instruction, an action history, a screenshot with its corresponding accessibility tree.\n High-level instruction: {high_level_instruction}\n Action history: {action_history}\n Accessibility tree: {a11y_tree}\n  Please generate the low-level thought and action for the next step."
 response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
 print(f'User: {question}\nAssistant: {response}')
 ```
 
 
-
-
 ## Citation
 If you find this repository helpful, feel free to cite our paper:
 ```bibtex
-@article{wu2024atlas,
-        title={OS-ATLAS: A Foundation Action Model for Generalist GUI Agents},
-        author={Wu, Zhiyong and Wu, Zhenyu and Xu, Fangzhi and Wang, Yian and Sun, Qiushi and Jia, Chengyou and Cheng, Kanzhi and Ding, Zichen and Chen, Liheng and Liang, Paul Pu and others},
-        journal={arXiv preprint arXiv:2410.23218},
-        year={2024}
-      }
+@article{sun2024osgenesis,
+  title={OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis},
+  author={Qiushi Sun and Kanzhi Cheng and Zichen Ding and Chuanyang Jin and Yian Wang and Fangzhi Xu and Zhenyu Wu and Chengyou Jia and Liheng Chen and Zhoumianze Liu and Ben Kao and Guohao Li and Junxian He and Yu Qiao and Zhiyong Wu},
+  journal={arXiv preprint arXiv:2412.19723},
+  year={2024}
+}
 ```