Update README.md

README.md

@@ -1,6 +1,22 @@
+🚗 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:
+
+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.
+🌐 Applications:
+
+Insurance claim processing
+Vehicle inspection automation
+Fleet management optimization
+🚀 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!
+# First Approach
 import numpy as np
 from PIL import Image
-# Load model directly
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 
 processor = AutoImageProcessor.from_pretrained("beingamit99/car_damage_detection")
@@ -10,12 +26,15 @@ inputs = processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
 logits = outputs.logits.detach().cpu().numpy()
 
-# Get the most likely class and its probability
 predicted_class_id = np.argmax(logits)
 predicted_proba = np.max(logits)
 
-# Get the class name from the model's label map
 label_map = model.config.id2label
 predicted_class_name = label_map[predicted_class_id]
 
-print(f"Predicted class: {predicted_class_name} (probability: {predicted_proba:.4f})")
+print(f"Predicted class: {predicted_class_name} (probability: {predicted_proba:.4f})")
+
+# 2nd Aprroach
+from transformers import pipeline
+
+pipe = pipeline("image-classification", model="beingamit99/car_damage_detection")