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Weather-Image-Classification

Weather-Image-Classification is a vision-language model fine-tuned from google/siglip2-base-patch16-224 for multi-class image classification. It is trained to recognize weather conditions from images using the SiglipForImageClassification architecture.

Classification Report:
 precision recall f1-score support

cloudy/overcast 0.8493 0.8762 0.8625 6702
 foggy/hazy 0.8340 0.8128 0.8233 1261
 rain/strom 0.7644 0.7592 0.7618 1927
 snow/frosty 0.8341 0.8448 0.8394 1875
 sun/clear 0.9124 0.8846 0.8983 6274

 accuracy 0.8589 18039
 macro avg 0.8388 0.8355 0.8371 18039
 weighted avg 0.8595 0.8589 0.8591 18039

👁 download (1).png

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Label Space: 5 Classes

The model classifies an image into one of the following weather categories:

"id2label": {
 "0": "cloudy/overcast",
 "1": "foggy/hazy",
 "2": "rain/storm",
 "3": "snow/frosty",
 "4": "sun/clear"
}

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Install Dependencies

pip install -q transformers torch pillow gradio

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Inference Code

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

# Load model and processor
model_name = "prithivMLmods/Weather-Image-Classification" # Replace with actual path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
id2label = {
 "0": "cloudy/overcast",
 "1": "foggy/hazy",
 "2": "rain/storm",
 "3": "snow/frosty",
 "4": "sun/clear"
}

def classify_weather(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_weather,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(num_top_classes=5, label="Weather Condition"),
 title="Weather-Image-Classification",
 description="Upload an image to identify the weather condition (sun, rain, snow, fog, or clouds)."
)

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

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Intended Use

Weather-Image-Classification is useful for:

• Automated weather tagging for photography and media.
• Enhancing dataset labeling in weather-related research.
• Supporting smart surveillance and traffic systems.
• Improving scene understanding in autonomous vehicles.

Base model

google/siglip2-base-patch16-224

(119)

Moderation, Balance, Classifiers • 6 items • Updated 1 day ago • 2