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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- traffic |
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- dense |
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- classification |
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--- |
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# **Traffic-Density-Classification** |
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> **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. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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high-traffic 0.8647 0.8410 0.8527 585 |
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low-traffic 0.8778 0.9485 0.9118 3803 |
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medium-traffic 0.7785 0.6453 0.7057 1187 |
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no-traffic 0.8730 0.7292 0.7946 528 |
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accuracy 0.8602 6103 |
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macro avg 0.8485 0.7910 0.8162 6103 |
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weighted avg 0.8568 0.8602 0.8559 6103 |
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``` |
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The model categorizes images into the following 4 classes: |
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- **Class 0:** "high-traffic" |
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- **Class 1:** "low-traffic" |
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- **Class 2:** "medium-traffic" |
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- **Class 3:** "no-traffic" |
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# **Run with Transformers🤗** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor |
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from transformers import SiglipForImageClassification |
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from transformers.image_utils import load_image |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Traffic-Density-Classification" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def traffic_density_classification(image): |
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"""Predicts traffic density category for an image.""" |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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labels = { |
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"0": "high-traffic", "1": "low-traffic", "2": "medium-traffic", "3": "no-traffic" |
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} |
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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=traffic_density_classification, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="Traffic Density Classification", |
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description="Upload an image to classify it into one of the 4 traffic density categories." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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# **Intended Use:** |
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The **Traffic-Density-Classification** model is designed for traffic image classification. It helps categorize traffic density levels into predefined categories. Potential use cases include: |
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- **Traffic Monitoring:** Classifying images from traffic cameras to assess congestion levels. |
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- **Smart City Applications:** Assisting in traffic flow management and congestion reduction strategies. |
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- **Automated Traffic Analysis:** Helping transportation authorities analyze and optimize road usage. |
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- **AI Research:** Supporting computer vision-based traffic density classification models. |
