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--- |
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license: apache-2.0 |
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datasets: |
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- drveronika/x_fake_profile_detection |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- bot-detection |
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- x |
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- twitter |
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- experimental |
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--- |
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# **x-bot-profile-detection** |
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> **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. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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bot 0.9912 0.9960 0.9936 2500 |
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cyborg 0.9940 0.9880 0.9910 2500 |
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real 0.8634 0.9936 0.9239 2500 |
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verified 0.9948 0.8460 0.9144 2500 |
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accuracy 0.9559 10000 |
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macro avg 0.9609 0.9559 0.9557 10000 |
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weighted avg 0.9609 0.9559 0.9557 10000 |
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``` |
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--- |
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## **Label Classes** |
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The model predicts one of the following profile types: |
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``` |
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0: bot → Automated accounts |
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1: cyborg → Partially automated or suspiciously mixed behavior |
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2: real → Genuine human users |
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3: verified → Verified accounts or official profiles |
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``` |
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--- |
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## **Installation** |
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```bash |
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pip install transformers torch pillow gradio |
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``` |
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--- |
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## **Example Inference Code** |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/x-bot-profile-detection" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# Define class mapping |
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id2label = { |
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"0": "bot", |
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"1": "cyborg", |
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"2": "real", |
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"3": "verified" |
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} |
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def detect_profile_type(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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prediction = { |
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id2label[str(i)]: round(probs[i], 3) for i in range(len(probs)) |
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} |
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return prediction |
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# Create Gradio UI |
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iface = gr.Interface( |
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fn=detect_profile_type, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=4, label="Predicted Profile Type"), |
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title="x-bot-profile-detection", |
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description="Upload a social media profile picture to classify it as Bot, Cyborg, Real, or Verified using a SigLIP2 model." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## **Use Cases** |
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* Social media moderation and automation detection |
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* Anomaly detection in public discourse |
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* Botnet analysis and influence operation research |
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* Platform integrity and trust verification |
