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--- |
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license: apache-2.0 |
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datasets: |
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- prithivMLmods/WeatherNet-05 |
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library_name: transformers |
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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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tags: |
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- Weather-Detection |
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- SigLIP2 |
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- 93M |
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--- |
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# Weather-Image-Classification |
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> 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. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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cloudy/overcast 0.8493 0.8762 0.8625 6702 |
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foggy/hazy 0.8340 0.8128 0.8233 1261 |
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rain/strom 0.7644 0.7592 0.7618 1927 |
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snow/frosty 0.8341 0.8448 0.8394 1875 |
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sun/clear 0.9124 0.8846 0.8983 6274 |
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accuracy 0.8589 18039 |
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macro avg 0.8388 0.8355 0.8371 18039 |
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weighted avg 0.8595 0.8589 0.8591 18039 |
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``` |
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--- |
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## Label Space: 5 Classes |
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The model classifies an image into one of the following weather categories: |
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```json |
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"id2label": { |
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"0": "cloudy/overcast", |
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"1": "foggy/hazy", |
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"2": "rain/storm", |
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"3": "snow/frosty", |
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"4": "sun/clear" |
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} |
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``` |
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--- |
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## Install Dependencies |
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```bash |
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pip install -q transformers torch pillow gradio |
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``` |
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--- |
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## Inference Code |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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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/Weather-Image-Classification" # Replace with actual path |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# Label mapping |
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id2label = { |
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"0": "cloudy/overcast", |
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"1": "foggy/hazy", |
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"2": "rain/storm", |
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"3": "snow/frosty", |
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"4": "sun/clear" |
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} |
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def classify_weather(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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prediction = { |
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id2label[str(i)]: round(probs[i], 3) for i in range(len(probs)) |
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} |
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return prediction |
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# Gradio Interface |
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iface = gr.Interface( |
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fn=classify_weather, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=5, label="Weather Condition"), |
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title="Weather-Image-Classification", |
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description="Upload an image to identify the weather condition (sun, rain, snow, fog, or clouds)." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## Intended Use |
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Weather-Image-Classification is useful for: |
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* Automated weather tagging for photography and media. |
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* Enhancing dataset labeling in weather-related research. |
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* Supporting smart surveillance and traffic systems. |
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* Improving scene understanding in autonomous vehicles. |
