WebClick-AgentBrowse-SigLIP2 is a vision-language encoder model fine-tuned from 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

[!note] agent-browse / calendars / human-browse

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

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

%%capture
!pip install datasets==3.2.0

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.