In the insurance industry, processing claims for vehicle damage is a common task.
With advancements in AI and Computer Vision, settling claims online by uploading damaged car images is now possible.

https://www.kaggle.com/datasets/imnandini/analytics-vidya-ripik-ai-hackfest

Training set (train.zip)
Test set (test.zip)
Sample submission (sample_submission.csv)

The training set contains a diverse dataset of car images with labels indicating the specific type of damage (e.g., dents, scratches, cracks).
The train.csv file includes the following columns:

• image_id: Unique identifier of the image
  
• filename: Filename of the image
  
• label: Type of damage present in the car
  
  1. Crack
  2. Scratch
  3. Tire Flat
  4. Dent
  5. Glass Shatter
  6. Lamp Broken

The test set contains only images, and the goal is to predict the type of damage for each image.
The test.csv file includes the following columns:

• image_id: Unique identifier of the image
• filename: Filename of the image

The solution file must contain predictions for every image_id in the test set. It must contain only 2 columns - image_id and label.
The solution file format must be similar to that of sample_submission.csv. sample_submission.csv contains 2 variables:

• image_id: Unique identifier of an image
• label: Type of damage present in the car {1:crack, 2:scratch, 3:tire flat, 4:dent, 5:glass shatter, 6:lamp broken}

The model will be evaluated based on the macro F1 score.

The project is organized into CRISP-DM phases for effective development and documentation.

1. Business Understanding
2. Data Understanding
3. Data Preparation
4. Modeling
5. Evaluation
6. Deployment
7. Conclusion

• GitHub Repo: Car Damage Image Classification Capstone Project
• Project Notebook: Car_Damage Image_Multi_Class_Classification.ipynb

Identifying fraudulent claims, especially those exaggerating damage, poses a challenge. The goal is to develop a high-performance model for automatic car damage classification, enabling insurance companies to assess claim legitimacy accurately.

Develop a model to automatically classify images of damaged cars into different types of damages for efficient claims processing and fraud detection.

• Insurance companies
• Claim processing teams

• Description of dataset acquisition.
• Dataset statistics.

• Visualizations of image samples and their labels.
• Insights into class distribution.

• Image resizing and normalization.
• Augmentation techniques applied.

• Keras offers pretrained models at keras.io
• I use the EfficientNetV2B0 model due to its fairly high Top-1 Accuracy and does not require depth.
• EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the [0, 255] range.

• Change Learning Rate
• Adding more layers
  • Conv2D
  • AveragePooling2D
  • SpatialDropout2D
  • Dropout
  • BatchNormalization

• Definition of evaluation metrics.
  • Submissions are evaluated using the probabilistic F1 score (pFbeta)
• Results on validation and test sets.

675/675 [==============================] - 598s 886ms/step - loss: 1.3841 - categorical_accuracy: 0.3993 - pFbeta: 0.3193 - precision: 0.5266 - recall: 0.1667 - val_loss: 1.5634 - val_categorical_accuracy: 0.3800 - val_pFbeta: 0.2988 - val_precision: 0.4104 - val_recall: 0.1400

• https://repost.aws/knowledge-center/lambda-container-images

Build docker image using the recommended public image for Lambda once Dockerfile has been created below:

docker build -t car-insurance-model .

To test first run image that was built:

docker run -it --rm -p 8080:8080 car-insurance-model:latest

# Tag the Existing Image, username/car-insurance-model:new-tag
docker tag car-insurance-model:latest developerhost/car-insurance-model:latest

# Push the newly tagged image to Docker Hub:
developerhost/car-insurance-model:latest

# you can pull the image:
docker pull developerhost/car-insurance-model:latest

lambda function a function must be added as below to the lambda_function.py file:

def lambda_handler(event, context):
    url = event['url']
    result = predict(url)
    return result

Run the file:

python client_to_docker_test.py

This is the output I recieved which clearly shows that the image was predicted as a "dent" which is correct:

{'crack': 0.006185653153806925,
 'scratch': 0.34056955575942993,
 'tire_flat': 0.021280569955706596,
 'dent': 0.5486962795257568,
 'glass_shatter': 0.0674322172999382,
 'lamp_broken': 0.01583569310605526}

output:

# python client_to_docker_test.py
# {"crack": 0.006185653153806925, "scratch": 0.34056955575942993, "tire_flat": 0.021280569955706596, "dent": 0.5486962795257568, "glass_shatter": 0.0674322172999382, "lamp_broken": 0.01583569310605526}