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---
license: apache-2.0
datasets:
- prithivMLmods/WeatherNet-05
library_name: transformers
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
tags:
- Weather-Detection
- SigLIP2
- 93M
---
# 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.
```py
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
```
---
## Label Space: 5 Classes
The model classifies an image into one of the following weather categories:
```json
"id2label": {
"0": "cloudy/overcast",
"1": "foggy/hazy",
"2": "rain/storm",
"3": "snow/frosty",
"4": "sun/clear"
}
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio
```
---
## Inference Code
```python
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()
```
---
## 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. |