Component Classification

About the client

Our client specializes in innovative solutions across industries, seeking advanced technologies to enhance operations. They aimed to develop a robust system for accurately classifying components from images in various conditions.

The Story

The project aimed to build an advanced component classification system using object detection. The challenge was to ensure high accuracy despite the variability in images, including different angles, scales, and artificial backgrounds. Achieving over 87% accuracy was key to the success of the project, with the system also recommending similar components from the dataset.

Challenges

• > Handling image variations (angle, scale, and background).

• > Ensuring robust performance with artificial images.

• > Achieving high classification accuracy (above 87%).

The Solution

A comprehensive tech stack was employed to develop the system. Python, Keras, and TensorFlow were used for deep learning, while SSD Mobilenet CNN provided the object detection framework. Kubeflow and GCP enabled scalable cloud-based deployment and training. scikit-learn was integrated for machine learning tasks, and Flask and OpenCV were used to build user-friendly interfaces and handle image processing.

The Outcome

The solution successfully met the objectives by accurately classifying components, even under varying conditions. The model achieved an accuracy rate of over 87%, demonstrating its robustness to different image conditions and artificial backgrounds. This project highlighted the effectiveness of deep learning and a well-integrated tech stack in solving complex challenges, with scalable applications across industries.