File size: 3,592 Bytes
e178a34 5591726 e178a34 5591726 e178a34 5591726 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- traffic
- dense
- classification
---
# **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.
```py
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
```
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🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
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. |