ML Image Classifier

A machine learning pipeline for image classification using Python and TensorFlow, trained on custom datasets with 95%+ accuracy.

Source

Context

Research project for automated quality control in manufacturing.

Role

ML Engineer

Goal

Achieve 95%+ classification accuracy on industrial components.

Overview

Built a complete ML pipeline for classifying industrial components using computer vision. The model uses a fine-tuned ResNet50 architecture trained on a custom dataset of 10,000+ images. Includes data augmentation, transfer learning, and a Flask API for inference.

Tech Stack

PythonTensorFlowOpenCVFlaskDocker

Key Features

Transfer learning with ResNet50
Custom data augmentation pipeline
REST API for real-time inference
Docker containerized deployment
Performance monitoring dashboard

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