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Traffic-Density-Classification

Traffic-Density-Classification is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify images into traffic density categories using the SiglipForImageClassification architecture.

Classification Report:
 precision recall f1-score support

 high-traffic 0.8647 0.8410 0.8527 585
 low-traffic 0.8778 0.9485 0.9118 3803
medium-traffic 0.7785 0.6453 0.7057 1187
 no-traffic 0.8730 0.7292 0.7946 528

 accuracy 0.8602 6103
 macro avg 0.8485 0.7910 0.8162 6103
 weighted avg 0.8568 0.8602 0.8559 6103

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The model categorizes images into the following 4 classes:

• Class 0: "high-traffic"
• Class 1: "low-traffic"
• Class 2: "medium-traffic"
• Class 3: "no-traffic"

Run with Transformers🤗

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Traffic-Density-Classification"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def traffic_density_classification(image):
 """Predicts traffic density category for an 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()
 
 labels = {
 "0": "high-traffic", "1": "low-traffic", "2": "medium-traffic", "3": "no-traffic"
 }
 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
 
 return predictions

# Create Gradio interface
iface = gr.Interface(
 fn=traffic_density_classification,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="Traffic Density Classification",
 description="Upload an image to classify it into one of the 4 traffic density categories."
)

# Launch the app
if __name__ == "__main__":
 iface.launch()

Intended Use:

The Traffic-Density-Classification model is designed for traffic image classification. It helps categorize traffic density levels into predefined categories. Potential use cases include:

• Traffic Monitoring: Classifying images from traffic cameras to assess congestion levels.
• Smart City Applications: Assisting in traffic flow management and congestion reduction strategies.
• Automated Traffic Analysis: Helping transportation authorities analyze and optimize road usage.
• AI Research: Supporting computer vision-based traffic density classification models.

Base model

google/siglip2-base-patch16-224

(119)

vit, siglip • 7 items • Updated 1 day ago • 1