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DOZE-GUARD-RLDD

DOZE-GUARD-RLDD [Real-Time Distracted Driver Detection] is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for binary image classification. It is trained to detect whether a person in the image is drowsy or non-drowsy using the SiglipForImageClassification architecture.

DOZE GUARD RLDD detection works best with crisp and high-quality images. Noisy images are not recommended for validation.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

Detection and Prediction of Driver Drowsiness for the Prevention of Road Accidents Using Deep Neural Networks Techniques https://www.researchgate.net/publication/353397807_Detection_and_Prediction_of_Driver_Drowsiness_for_the_Prevention_of_Road_Accidents_Using_Deep_Neural_Networks_Techniques

Classification Report:
 precision recall f1-score support

 Drowsy 0.9818 0.9952 0.9885 17868
 Non Drowsy 0.9945 0.9788 0.9866 15566

 accuracy 0.9876 33434
 macro avg 0.9881 0.9870 0.9875 33434
weighted avg 0.9877 0.9876 0.9876 33434

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Label Space: 2 Classes

The model classifies an image as either:

Class 0: Drowsy
Class 1: Non Drowsy

Install Dependencies

pip install -q transformers torch pillow gradio hf_xet

Inference Code

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

# Load model and processor
model_name = "prithivMLmods/DOZE-GUARD-RLDD" # Replace with your model path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
id2label = {
 "0": "Drowsy",
 "1": "Non Drowsy"
}

def classify_drowsiness(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=classify_drowsiness,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(num_top_classes=2, label="Drowsiness Detection"),
 title="DOZE-GUARD-RLDD",
 description="Upload an image to classify whether the person is drowsy or non-drowsy."
)

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

Demo Inference

👁 Screenshot 2025-05-14 at 19-20-23 DOZE-GUARD-RLDD.png
👁 Screenshot 2025-05-14 at 19-05-19 DOZE-GUARD-RLDD.png
👁 Screenshot 2025-05-14 at 19-06-47 DOZE-GUARD-RLDD.png

Intended Use

DOZE-GUARD-RLDD is useful in scenarios such as:

• Driver Monitoring – Detect drowsiness in drivers to prevent accidents.
• Workplace Safety – Monitor employee alertness to improve safety in high-risk environments.
• Healthcare – Assist in diagnosing conditions related to sleep deprivation or drowsiness.
• Surveillance – Real-time monitoring of individuals for drowsiness detection in critical areas.

Base model