AI-Powered Image Classification with Vertex AI & AutoML- Damaged Car

This project focuses on building an AI-powered image classification model using Google Cloud Vertex AI and AutoML Vision. The solution automates the detection and categorization of images such as product photos, medical scans, or document types helping businesses speed up visual data analysis and improve accuracy in classification workflows.

Use Cases

Manual image review is time-consuming, inconsistent, and not scalable. Businesses in retail, healthcare, and logistics need reliable ways to:

  • Classify thousands of images quickly

  • Detect patterns or anomalies

  • Automate tagging, sorting, or verification processes

Traditional rule-based image handling lacks adaptability and fails to scale across diverse datasets.


Case Study: Damaged Car Identification for an Insurance Company

The company needed to classify images of damaged cars

  • Uploaded 500+ labeled images to Vertex AI.

  • AutoML trained the model in under an hour.

  • Achieved 92% classification accuracy, automating 80% of the manual review workload.

Technical Stack


  • ML Platform: Vertex AI AutoML Vision

  • Data Storage: Google Cloud Storage

  • Model Serving: Vertex AI Endpoints

  • Visualization: BigQuery, Looker Studio

  • Integration: REST API for application deployment

Process

1. Dataset Preparation & Labeling

  • Upload images to Google Cloud Storage.

  • Organize into folders based on category (e.g., bumper engine_compartment, hood lateral windshield).

  • Use Vertex AI Dataset service to import, label, and split the data (train/test).

2. Model Training with AutoML Vision

  • Vertex AI AutoML Vision automatically builds and tunes a classification model.

  • It applies transfer learning and evaluates with metrics like precision, recall, and F1 score.

3. Model Deployment & Predictions

  • Deploy the trained model on Vertex AI endpoint.

  • Send prediction requests via the Vertex AI API.

  • The model returns the predicted label with a confidence score.

4. Results Visualization & Usage

  • Use Vertex AI dashboard to monitor performance.

  • Export predictions to BigQuery or visualize via Looker Studio.

  • Integrate into applications via REST API.

Why Businesses Need This AI Solution

  • 95% Time Savings: Automates what would take hours of manual work.

  • Consistent Accuracy: Reduces human error and improves confidence.

  • Seamless Integration: Easy to embed into existing workflows and apps.

Training Data
Training Data
Training Data
Creating Model
Creating Model
Creating Model
Sample Model
Sample Model
Sample Model

Performance & Results


  • Model Accuracy: Up to 93% with AutoML Vision

  • Training Time: < 1 hour for datasets under 1000 images

  • Prediction Latency: < 1 second per request

  • Operational Efficiency: Reduced manual effort by 80%

Impact


  • 95% Time Savings: Automates what would take hours of manual work.

  • Consistent Accuracy: Reduces human error and improves confidence.

  • Seamless Integration: Easy to embed into existing workflows and apps.

  • No ML Expertise Required: AutoML handles the heavy lifting.

Want to Automate Image Analysis with AI?

📩 Hire me to design and deploy AI-powered image classifiers tailored to your use case.

  • Boost productivity with automated image recognition.

  • Achieve faster decision-making with visual data insights.

  • Integrate scalable AI solutions without building models from scratch.

🚀 Let’s bring intelligent visual automation to your business!

© 2025 Lehar. All Rights Reserved.

© 2025 Lehar. All Rights Reserved.