CNN Image Classifier for Household Objects
Deep learning model achieving 93.4% accuracy in classifying 4 household object types
Overview
Implemented Convolutional Neural Network with Transfer Learning (ResNet18) for classifying four household objects: cooking pots, cups, knives, and pens. Achieved 93.4% overall accuracy with comprehensive data augmentation, rigorous evaluation, and detailed per-class performance analysis. The model demonstrates particularly strong performance on cooking pots (98.4%) and cups (96.8%).
Problem
Need for accurate automated classification of household objects for inventory management and object recognition applications
Solution
Implemented CNN with Transfer Learning (ResNet18), data augmentation, class-specific optimization, and rigorous evaluation metrics
Results
Overall Test Accuracy: 93.4%
Cooking Pot: 98.4% accuracy
Cup: 96.8% accuracy
Pen: 94.6% accuracy
Knife: 93.1% accuracy
Comprehensive confusion matrix analysis
Robust performance across all object classes