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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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- fire-detection |
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--- |
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# **Fire-Detection-Siglip2** |
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**Fire-Detection-Siglip2** 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 detect fire, smoke, or normal conditions using the SiglipForImageClassification architecture. |
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The model categorizes images into three classes: |
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- **Class 0:** "Fire" – The image shows active fire. |
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- **Class 1:** "Normal" – The image depicts a normal, fire-free environment. |
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- **Class 2:** "Smoke" – The image contains visible smoke, indicating potential fire risk. |
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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/Fire-Detection-Siglip2" |
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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 fire_detection(image): |
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"""Classifies an image as fire, smoke, or normal conditions.""" |
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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 = model.config.id2label |
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predictions = {labels[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=fire_detection, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Detection Result"), |
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title="Fire Detection Model", |
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description="Upload an image to determine if it contains fire, smoke, or a normal condition." |
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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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Classification report: |
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precision recall f1-score support |
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fire 0.9940 0.9881 0.9911 1010 |
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normal 0.9892 0.9941 0.9916 1010 |
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smoke 0.9990 1.0000 0.9995 1010 |
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accuracy 0.9941 3030 |
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macro avg 0.9941 0.9941 0.9941 3030 |
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weighted avg 0.9941 0.9941 0.9941 3030 |
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# **Intended Use:** |
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The **Fire-Detection-Siglip2** model is designed to classify images into three categories: **fire, smoke, or normal conditions**. It helps in early fire detection and environmental monitoring. |
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### Potential Use Cases: |
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- **Fire Safety Monitoring:** Detecting fire and smoke in surveillance footage. |
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- **Early Warning Systems:** Helping in real-time fire hazard detection in public and private areas. |
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- **Disaster Prevention:** Assisting emergency response teams by identifying fire-prone areas. |
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- **Smart Home & IoT Integration:** Enhancing automated fire alert systems in smart security setups. |
