Create README.md

README.md

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+---
+license: mit
+base_model:
+- microsoft/Florence-2-large
+library_name: transformers
+tags:
+- GUI
+- VLM
+- Agent
+- GUI-Grounding
+---
+
+
+# 🎯 GoClick-Large: Super Fast Lightweight GUI Grounding Expert
+
+
+<div align="center">
+  
+[![GitHub](https://img.shields.io/badge/GitHub-Repo-black?logo=github)](https://github.com/ZJULiHongxin/GoClick)
+[![Paper](https://img.shields.io/badge/Paper-GoClick-blue?logo=adobeacrobatreader)](https://arxiv.org/abs/2604.23941)
+[![GoClickLarge](https://img.shields.io/badge/🤗%20GoClickLarge-Model-yellow)](https://huggingface.co/HongxinLi/GoClick-Large)
+[![GoClickBase](https://img.shields.io/badge/🤗%20GoClickBase-Model-yellow)](https://huggingface.co/HongxinLi/GoClick-Base)
+[![SFTData](https://img.shields.io/badge/🤗%20SFT-Dataset-yellow)](https://huggingface.co/datasets/HongxinLi/GoClick_Coreset_3814k)
+[![SFTZipData](https://img.shields.io/badge/🤗%20SFTZip-SFTData-yellow)](https://huggingface.co/datasets/HongxinLi/GoClick_sft_data)
+
+</div>
+
+
+GoClick is a state-of-the-art two-stage framework for precise UI element grounding. Built on the Florence-2 architecture, it bridges the gap between high-level intent and low-level pixel coordinates by separating the Planning and Grounding tasks.
+
+## 🏗️ Agent Architecture Overview
+
+1. Stage 1 (Planning): Analyze UI screenshot + Goal -> Output Function Description.
+2. Stage 2 (Grounding): Screenshot + Function Description -> Output Precise Coordinates.Note: This model is the specialized Stage 2 Grounder, fine-tuned for extreme precision in locating elements based on their described functionality.
+
+## 🚀 Quick Start (Inference of The Model)
+
+Prerequisites
+
+```
+pip install transformers==4.45.0 timm
+```
+
+Note: The version of Transformers should not be too high. Adjust the version if model loading fails.
+
+### Usage Example
+
+```
+from transformers import AutoModelForCausalLM, AutoProcessor
+from PIL import Image
+
+
+def postprocess(text: str, image_size: tuple[int]):
+    """Function that decodes model's generation into action json.
+
+    Args:
+        text: single generated sample
+        image_size: corresponding image size
+    """
+    point_pattern = r"<loc_(\d+)>,<loc_(\d+)>"
+
+    try:
+        location = re.findall(point_pattern, text)[0]
+        if len(location) > 0:
+            point = [int(loc) for loc in location]
+
+    except Exception:
+        point = (0, 0)
+
+    return point
+
+# Load model and processor
+model = AutoModelForCausalLM.from_pretrained("HongxinLi/GoClick-Base", trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("HongxinLi/GoClick-Base", trust_remote_code=True)
+
+# Load UI screenshot
+image = Image.open("ui_screenshot.png")
+
+# Stage 1: Planning
+
+# Functionality Grounding (For AutoGUI FuncPred Benchmark)
+planning_prompt = f"Locate the element according to its detailed functionality description. {goal_info} (Output the center coordinates of the target)"
+
+# Intent Grounding (For RefExp, MOTIF, and VisualWebBench Action Grounding)
+planning_prompt = f"I want to {goal_info}. Please locate the target element I should interact with. (Output the center coordinates of the target)"
+
+# Description Grounding (For ScreenSpot/v2 and VisualWebBench Element Grounding))
+planning_prompt = f"Where is the {goal_info} element? (Output the center coordinates of the target)"
+
+
+inputs = processor(
+    images=image,
+    text=prompt,
+    return_tensors="pt",
+    do_resize=True,
+).to(model.device, dtype=model.dtype)
+
+outputs = model.generate(
+            **inputs,
+            do_sample= False,
+            max_new_tokens=max_new_tokens,
+            use_cache=True
+        )
+
+text_output = processor.tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
+text_output = postprocess(text_output, img_size)
+
+```
+
+### 📊 Benchmarks
+
+GoClick-Base also achieves a good tradeoff between GUI element grounding accuracy and inference latency:
+
+| Model | Size | TTFT ↓ (ms) | TPOT ↓ (ms/token) | FuncPred (F; M, W) | ScreenSpot (B; M, W, D) | ScreenSpot-v2 (B; M, W, D) | MOTIF (I; M) | RefExp (I; M) | VWB EG (T; W) | VWB AG (I; W) |
+|-------|------|-------------|-------------------|--------------------|-------------------------|---------------------------|--------------|---------------|---------------|---------------|
+| GPT-4o | - | - | - | 9.8 | 17.8 | 20.4 | 30.5 | 21.8 | 5.6 | 6.8 |
+| Qwen2VL-7B | 8B | 118.9 | 21.2 | 38.7 | 66.4 | 66.9 | 75.1 | 64.8 | 55.9 | 62.1 |
+| CogAgent | 18B | 1253.2 | 208.8 | 29.3 | 47.4 | 49.2 | 46.7 | 35.0 | 55.7 | 59.2 |
+| SeeClick | 10B | 160.4 | 184.4 | 19.8 | 53.4 | 54.0 | 11.1 | 58.1 | 39.2 | 27.2 |
+| Ferret-UI | 8B | 152.5 | 22.9 | 1.2 | 7.1 | 7.8 | 15.9 | 5.5 | 3.9 | 1.9 |
+| UGround | 7B | 1034.6 | 27.9 | 48.8 | 74.8 | 76.5 | 72.4 | 73.6 | 85.2 | 63.1 |
+| OS-ATLAS-8B | 8B | 137.5 | 19.9 | 52.1 | 82.5 | 84.1 | 78.8 | 66.5 | 82.6 | 69.9 |
+| Aguvis | 8B | 119.7 | 21.2 | 52.0 | 83.8 | 85.6 | 73.8 | 80.9 | 91.3 | 68.0 |
+| Qwen2-VL | 2B | 58.8 | 16.4 | 7.1 | 17.9 | 18.6 | 28.8 | 29.2 | 17.9 | 17.5 |
+| OS-ATLAS-4B | 4B | 137.3 | 31.4 | 44.6 | 66.8 | 68.7 | 75.4 | 77.1 | 47.7 | 58.3 |
+| Ferret-UI | 3B | 69.5 | 9.8 | 1.3 | 2.1 | 1.9 | 5.5 | 1.1 | 0.7 | 1.0 |
+| ShowUI | 2B | 79.7 | 14.7 | 39.9 | 76.1 | 77.4 | 72.3 | 58.4 | 64.2 | 55.3 |
+| **GoClick-L (ours)** | 0.8B | 91.1 | 8.3 | **69.5** | **78.5** | **81.1** | **80.4** | **78.2** | **90.3** | **68.0** |
+| **GoClick-B (ours)** | 0.2B | **37.7** | **4.1** | 64.4 | 74.1 | 75.2 | 76.8 | 71.9 | 90.3 | 61.2 |
+
+
+## 📝 Citation
+If you use GoClick in your research, please cite our paper:
+
+```
+@misc{li2026goclicklightweightelementgrounding,
+      title={GoClick: Lightweight Element Grounding Model for Autonomous GUI Interaction}, 
+      author={Hongxin Li and Yuntao Chen and Zhaoxiang Zhang},
+      year={2026},
+      eprint={2604.23941},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2604.23941}, 
+}
+```