π Car Damage Prediction Model π 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 π
- Accurate classification into six car damage categories.
- Seamless integration into various applications.
- Streamlined image processing with transformer-based architecture.
Applications π
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! π
How to Use This Model π€
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)
- Downloads last month
- 860
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.