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πŸš— Car Damage Prediction Model πŸ› οΈ

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 πŸ”

  • 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 πŸ€–

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)
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