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---
license: apache-2.0
datasets:
- Hcompany/WebClick
base_model:
- google/siglip2-base-patch16-224
language:
- en
pipeline_tag: image-classification
library_name: transformers
tags:
- agentbrowse
- calendars
- humanbrowse
- SigLIP2
---
# **WebClick-AgentBrowse-SigLIP2**
> **WebClick-AgentBrowse-SigLIP2** is a vision-language encoder model fine-tuned from [`google/siglip2-base-patch16-224`](https://huggingface.co/google/siglip2-base-patch16-224) for **multi-class image classification**.
It is trained to detect and classify web UI click regions into three classes: `agentbrowse`, `calendars`, and `humanbrowse`. The model utilizes the `SiglipForImageClassification` architecture.
> \[!note]
> **SigLIP 2**: *Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
> [!note]
agent-browse / calendars / human-browse
---
```py
Classification Report:
precision recall f1-score support
agentbrowse 0.9556 0.8763 0.9142 590
calendars 0.9707 0.9413 0.9558 528
humanbrowse 0.8481 0.9539 0.8979 521
accuracy 0.9219 1639
macro avg 0.9248 0.9238 0.9226 1639
weighted avg 0.9263 0.9219 0.9224 1639
```
---
## Label Space: 3 Classes
```
Class 0: agentbrowse
Class 1: calendars
Class 2: humanbrowse
````
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
````
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/WebClick-AgentBrowse-SigLIP2" # Replace with actual HF model repo
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "agentbrowse",
"1": "calendars",
"2": "humanbrowse"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=3, label="Click Type Classification"),
title="WebClick AgentBrowse Classifier",
description="Upload a web UI screenshot to classify regions: agentbrowse, calendars, or humanbrowse."
)
if __name__ == "__main__":
iface.launch()
```
---
## ID2Label Testing
```py
%%capture
!pip install datasets==3.2.0
```
```py
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("Hcompany/WebClick")
# Extract unique masterCategory values (assuming it's a string field)
labels = sorted(set(example["bucket"] for example in dataset["test"]))
# Create id2label mapping
id2label = {str(i): label for i, label in enumerate(labels)}
# Print the mapping
print(id2label)
```
```
{'0': 'agentbrowse', '1': 'calendars', '2': 'humanbrowse'}
```
---
## Intended Use
**WebClick-AgentBrowse-SigLIP2** is intended for:
* **UI Understanding** – Classify user interaction zones in web interface screenshots.
* **Multimodal Agents** – Enhance visual perception for agent planning or RPA systems.
* **Interface Automation** – Facilitate click zone detection for automated agents.
* **Web Analytics** – Analyze user behavior patterns based on layout interaction predictions. |