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README.md

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-🚗 Car Damage Predictor with llm VIT BEIT Architecture
-
-Unleash the power of cutting-edge vision transformers for precise car damage prediction! Introducing our fine-tuned model based on llm VIT BEIT, designed to classify car damage into six distinct classes with remarkable accuracy.
-
-🔍 Key Features:
-
-<b>Advanced Vision Transformer (VIT): Benefit from the state-of-the-art llm VIT BEIT architecture, optimized for superior image understanding and feature extraction.
-Six-Class Prediction: Our model excels in distinguishing between six different classes of car damage, providing detailed insights for comprehensive analysis.
-Robust Fine-Tuning: Trained on diverse and extensive datasets, the model exhibits robust performance across various scenarios and damage types.
-Hugging Face Integration: Seamlessly integrate our model into your workflow using Hugging Face, the go-to platform for efficient and user-friendly model deployment.
-<br> <br>
-🌐 Applications:
-</br>
-Insurance claim processing <br>
-Vehicle inspection automation <br>
-Fleet management optimization <br> <br>
-🚀 Enhance your car damage prediction tasks with the llm VIT BEIT model. Experience unprecedented accuracy and reliability for a wide range of applications. Get started with Hugging Face integration today!
-<br>
-# How to use this model
-
-# First Approach
-<CODE>
+# 🚗 Car Damage Prediction Model 🛠️
+
+Predict car damage with confidence using the **llm VIT bEIT** model! This model is trained to classify car damage into six distinct classes:
+
+- **"0"**: *Crack*
+- **"1"**: *Scratch*
+- **"2"**: *Tire Flat*
+- **"3"**: *Dent*
+- **"4"**: *Glass Shatter*
+- **"5"**: *Lamp Broken*
+
+## Key Features 🔍
+
+- Accurate classification into six car damage categories.
+- Seamless integration into various applications.
+- Streamlined image processing with transformer-based architecture.
+
+## Applications 🌐
+
+This powerful car damage prediction model can be seamlessly integrated into various applications, such as:
+
+- **Auto Insurance Claim Processing:** Streamline the assessment of car damage for faster claim processing.
+- **Vehicle Inspection Services:** Enhance efficiency in vehicle inspection services by automating damage detection.
+- **Used Car Marketplaces:** Provide detailed insights into the condition of used cars through automated damage analysis.
+
+Feel free to explore and integrate this model into your applications for accurate car damage predictions! 🌟
+
+
+## How to Use This Model 🤖
+
+### Approach
+
+### First Approach
+<code>
 import numpy as np 
 from PIL import Image
 from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+# Load the model and image processor
 processor = AutoImageProcessor.from_pretrained("beingamit99/car_damage_detection")
 model = AutoModelForImageClassification.from_pretrained("beingamit99/car_damage_detection")
+
+# Load and process the image
 image = Image.open(IMAGE)
 inputs = processor(images=image, return_tensors="pt")
+
+# Make predictions
 outputs = model(**inputs)
 logits = outputs.logits.detach().cpu().numpy()
 predicted_class_id = np.argmax(logits)
 predicted_proba = np.max(logits)
 label_map = model.config.id2label
 predicted_class_name = label_map[predicted_class_id]
+
+# Print the results
 print(f"Predicted class: {predicted_class_name} (probability: {predicted_proba:.4f})")
-</CODE>  <br></br>
+</code>
 
-# Second Aprroach
-<CODE>
+### Second Approach
+<code>
 from transformers import pipeline
 
+# Create a classification pipeline
 pipe = pipeline("image-classification", model="beingamit99/car_damage_detection")
-</CODE>
+pipe(IMAGE)
+</code>