🚗 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)