Update README.md

README.md

@@ -2,7 +2,28 @@
 license: apache-2.0
 datasets:
 - Shravanig/fire_detection_final
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-512
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Forest-Fire-Detection
+- SigLIP2
+- climate
+- Smoke
+- Normal
+- Fire
 ---
+![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/E4Cd-Kbj9wUkI9t_UOqE8.png)
+
+# Forest-Fire-Detection
+
+> `Forest-Fire-Detection` is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to detect whether an image contains **fire**, **smoke**, or a **normal** (non-fire) scene. The model uses the `SiglipForImageClassification` architecture.
+
+> [!note]
+SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features : https://arxiv.org/pdf/2502.14786
 
 ```py
 Classification Report:
@@ -18,3 +39,83 @@ weighted avg     0.9952    0.9952    0.9952      6060
 ```
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/xbB-O5F_pT10R9rLah_R3.png)
+
+---
+
+## Label Space: 3 Classes
+
+```
+Class 0: Fire  
+Class 1: Normal  
+Class 2: Smoke
+```
+
+---
+
+## Install Dependencies
+
+```bash
+pip install -q transformers torch pillow gradio hf_xet
+```
+
+---
+
+## 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/forest-fire-detection"  # Update with actual model name on Hugging Face
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Updated label mapping
+id2label = {
+    "0": "Fire",
+    "1": "Normal",
+    "2": "Smoke"
+}
+
+def classify_image(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_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=3, label="Forest Fire Detection"),
+    title="Forest-Fire-Detection",
+    description="Upload an image to detect whether the scene contains fire, smoke, or is normal."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## Intended Use
+
+`Forest-Fire-Detection` is designed for:
+
+* **Wildfire Monitoring** – Rapid identification of forest fire and smoke zones.
+* **Environmental Protection** – Surveillance of forest areas for early fire warning.
+* **Disaster Management** – Support in emergency response and evacuation decisions.
+* **Smart Surveillance** – Integrate with drones or camera feeds for automated fire detection.
+* **Research and Analysis** – Analyze visual datasets for fire-prone region identification.