README.md

---
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}, 
}
```