cardamagemodel

🚗 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

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}")

Second Approach

from transformers import pipeline
#Create a classification pipeline
pipe = pipeline("image-classification", model="beingamit99/car_damage_detection")
pipe(IMAGE)