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---
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.
```py
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**
```bash
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** |