README.md

metadata

license: apache-2.0
datasets:
  - alecsharpie/nailbiting_classification
language:
  - en
base_model:
  - google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
  - Nailbiting
  - Human
  - Behaviour
  - siglip2

NailbitingNet

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

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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/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