NailbitingNet is a binary image classification model based on google/siglip2-base-patch16-224, designed to detect nail-biting behavior in images. Leveraging the SiglipForImageClassification architecture, this model is ideal for behavior monitoring, wellness applications, and human activity recognition.

Classification Report:
              precision    recall  f1-score   support

      biting     0.8412    0.9076    0.8731      2824
   no biting     0.9271    0.8728    0.8991      3805

    accuracy                         0.8876      6629
   macro avg     0.8841    0.8902    0.8861      6629
weighted avg     0.8905    0.8876    0.8881      6629

Label Classes

The model distinguishes between:

Class 0: "biting"         → The person appears to be biting their nails  
Class 1: "no biting"      → No nail-biting behavior detected

Installation

pip install transformers torch pillow gradio

Example 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/NailbitingNet"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# ID to label mapping
id2label = {
    "0": "biting",
    "1": "no biting"
}

def detect_nailbiting(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=detect_nailbiting,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=2, label="Nail-Biting Detection"),
    title="NailbitingNet",
    description="Upload an image to classify whether the person is biting their nails or not."
)

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

Use Cases

Wellness & Habit Monitoring
Behavioral AI Applications
Mental Health Tools
Dataset Filtering for Behavior Recognition