Enhance model card for UI-AGILE: Add metadata, links, abstract, and usage

README.md

@@ -1,3 +1,161 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# UI-AGILE: Advancing GUI Agents with Effective Reinforcement Learning and Precise Inference-Time Grounding
+
+<div align="center">
+
+[[📖 Paper](https://huggingface.co/papers/2507.22025)] [[🚀 GitHub](https://github.com/KDEGroup/UI-AGILE)] [[🌐 Homepage](https://osatlas.github.io/)]
+
+</div>
+
+## Abstract
+
+The emergence of Multimodal Large Language Models (MLLMs) has driven significant advances in Graphical User Interface (GUI) agent capabilities. Nevertheless, existing GUI agent training and inference techniques still suffer from a dilemma for reasoning designs, ineffective reward, and visual noise. To address these issues, we introduce UI-AGILE for enhancing GUI agents at both training and inference. For training, we propose a suite of improvements to the Supervised Fine-Tuning (SFT) process: 1) a continuous reward function to incentivize high-precision grounding; 2) a "Simple Thinking" reward to balance planning with speed and grounding accuracy; and 3) a cropping-based resampling strategy to mitigate the sparse reward problem and improve learning on complex tasks. For inference, we present decomposed grounding with selection to dramatically improve grounding accuracy on high-resolution displays by breaking the image into smaller, manageable parts. Experiments show that UI-AGILE achieves the state-of-the-art grounding performance on two benchmarks ScreenSpot-Pro and ScreenSpot-v2 while it also exhibits strong general agent capabilities. For instance, using both our training and inference enhancement methods brings 23% grounding accuracy improvement over the best baseline on ScreenSpot-Pro. We provide the code in this https URL .
+
+## Overview
+
+UI-AGILE enhances GUI agents through improved training with a Continuous Reward function, Simple Thinking reward, and **Cropping-based Resampling**, and inference with **Decomposed Grounding with Selection**.
+
+<img src="https://github.com/user-attachments/assets/cf2ee020-5e15-4087-9a7e-75cc43662494" alt="UI-AGILE Overview">
+
+Trained on about only **9k** samples for just **2 epochs**, UI-AGILE shows superior performance, while also showcasing strong general agent capabilities. Furthermore, our inference method can act as a **plug-and-play enhancement** for a wide range of existing agents, improving the accuracy of some existing open-source models.
+
+As a baseline, the standard grounding approach applied to UI-AGILE-7B completes the benchmark in **30 minutes**. When applying our method, the decomposed grounding stage takes **35 minutes**. The subsequent VLM-based selection stage requires additional **4 minutes**. The modest increase in overhead is a practical trade-off for the substantial gain of grounding accuracy brought by our method.
+
+## Quick Start
+
+This section provides instructions on how to inference our pre-trained grounding models. 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.
+
+### Using OS-Atlas-Base-4B
+
+First, install the `transformers` library:
+```bash
+pip install transformers
+```
+For additional dependencies, please refer to the [InternVL2 documentation](https://internvl.readthedocs.io/en/latest/get_started/installation.html)
+
+Inference code example:
+```python
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section in the GitHub repo.
+path = 'KDEGroup/UI-AGILE'
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True).eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
+
+# set the max number of tiles in `max_num`
+pixel_values = load_image('./examples/images/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).\
+\\\"'Champions League' link\\\"\""
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+print(f'User: {question}\
+Assistant: {response}')
+```
+
+## Citation
+
+If you find this project useful, welcome to cite us.
+
+```bibtex
+@misc{lian2025uiagileadvancingguiagents,
+      title={UI-AGILE: Advancing GUI Agents with Effective Reinforcement Learning and Precise Inference-Time Grounding}, 
+      author={Shuquan Lian and Yuhang Wu and Jia Ma and Zihan Song and Bingqi Chen and Xiawu Zheng and Hui Li},
+      year={2025},
+      eprint={2507.22025},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2507.22025}, 
+}
+```
+
+## Acknowledgements
+
+We sincerely thank projects [R1-V](https://github.com/Deep-Agent/R1-V), [Open-R1](https://github.com/huggingface/open-r1), and [Open-r1-multimodal](https://github.com/EvolvingLMMs-Lab/open-r1-multimodal), [VLM-R1](https://github.com/om-ai-lab/VLM-R1) for providing their open-source resources.