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README.md

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+# 🌍 Disaster Image Classification using Vision Transformer
+
+This project uses a fine-tuned Vision Transformer (ViT) model to classify disaster-related images into various categories such as **Water Disaster**, **Fire Disaster**, **Human Damage**, etc.
+
+---
+
+## 🚀 Installation
+
+Install the required Python packages:
+
+```bash
+pip install transformers torch torchvision pillow requests
+```
+
+---
+
+## 🔍 Quick Start
+
+Use the pipeline to classify an image directly from a URL:
+
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Load the image classification pipeline
+pipe = pipeline("image-classification", model="Luwayy/disaster_images_model")
+
+# Load an image from a URL
+url = 'https://www.spml.co.in/Images/blog/wdt&c-152776632.jpg'
+response = requests.get(url)
+image = Image.open(BytesIO(response.content))
+
+# Classify the image
+results = pipe(image)
+
+# Print results
+print(results)
+```
+
+**Example Output:**
+```json
+[
+  {"label": "Water_Disaster", "score": 0.9184},
+  {"label": "Land_Disaster", "score": 0.0200},
+  {"label": "Non_Damage", "score": 0.0169},
+  {"label": "Human_Damage", "score": 0.0164},
+  {"label": "Fire_Disaster", "score": 0.0143}
+]
+```
+
+---
+
+## 🧠 Model Details
+
+- **Base Model:** `google/vit-base-patch16-224-in21k`
+- **Architecture:** Vision Transformer (`ViTForImageClassification`)
+- **Image Size:** 224x224
+- **Classes:**
+  - `Damaged_Infrastructure`
+  - `Fire_Disaster`
+  - `Human_Damage`
+  - `Land_Disaster`
+  - `Non_Damage`
+  - `Water_Disaster`
+
+---
+
+## ⚙️ Training Configuration
+
+- **Image Normalization:** Mean `[0.5, 0.5, 0.5]`, Std `[0.5, 0.5, 0.5]`
+- **Resize Method:** Bilinear to `224x224`
+- **Augmentations:** Resize, Normalize, Convert to Tensor
+- **Batch Size:** 16
+- **Epochs:** 3
+- **Learning Rate:** `3e-5`
+- **Weight Decay:** `0.01`
+- **Evaluation Strategy:** Per epoch
+