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x-bot-profile-detection

x-bot-profile-detection is a SigLIP2-based classification model designed to detect profile authenticity types on social media platforms (such as X/Twitter). It categorizes a profile image into four classes: bot, cyborg, real, or verified. Built on google/siglip2-base-patch16-224, the model leverages advanced vision-language pretraining for robust image classification.

Classification Report:
 precision recall f1-score support

 bot 0.9912 0.9960 0.9936 2500
 cyborg 0.9940 0.9880 0.9910 2500
 real 0.8634 0.9936 0.9239 2500
 verified 0.9948 0.8460 0.9144 2500

 accuracy 0.9559 10000
 macro avg 0.9609 0.9559 0.9557 10000
weighted avg 0.9609 0.9559 0.9557 10000

👁 download.png

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Label Classes

The model predicts one of the following profile types:

0: bot → Automated accounts 
1: cyborg → Partially automated or suspiciously mixed behavior 
2: real → Genuine human users 
3: verified → Verified accounts or official profiles

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Installation

pip install transformers torch pillow gradio

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Example Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/x-bot-profile-detection"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Define class mapping
id2label = {
 "0": "bot",
 "1": "cyborg",
 "2": "real",
 "3": "verified"
}

def detect_profile_type(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

# Create Gradio UI
iface = gr.Interface(
 fn=detect_profile_type,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(num_top_classes=4, label="Predicted Profile Type"),
 title="x-bot-profile-detection",
 description="Upload a social media profile picture to classify it as Bot, Cyborg, Real, or Verified using a SigLIP2 model."
)

if __name__ == "__main__":
 iface.launch()

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Use Cases

• Social media moderation and automation detection
• Anomaly detection in public discourse
• Botnet analysis and influence operation research
• Platform integrity and trust verification

Base model

google/siglip2-base-patch16-224

(119)

Moderation, Balance, Classifiers • 7 items • Updated 1 day ago • 1