This project focuses on classifying images of products into five categories using Convolutional Neural Networks (CNNs). The dataset consists of 5000 images, each belonging to one of the following categories:
- Cell Phones and Accessories
- Clothing Men
- Electronics
- Shoes
- Watches
I experimented with 6 different CNN models: Xception, VGG16, VGG19, ResNet50, MobileNetV2, and DenseNet121. The results of these experiments are summarized in the tables below.
Model | Train Accuracy | Validation Accuracy | Test Accuracy |
---|---|---|---|
Xception | 0.96 | 0.74 | 0.69 |
VGG-16 | 1 | 0.89 | 0.86 |
VGG-19 | 0.98 | 0.88 | 0.85 |
ResNet50 | 1 | 0.91 | 0.9 |
MobileNetV2 | 1 | 0.85 | 0.85 |
DenseNet121 | 0.97 | 0.78 | 0.77 |
The ResNet50 model achieved the highest accuracy among the evaluated models.
Label | Precision | Recall | F1-Score |
---|---|---|---|
Cell Phones and Accessories | 0.83 | 0.85 | 0.84 |
Clothing Men | 0.92 | 0.92 | 0.92 |
Electronics | 0.85 | 0.84 | 0.85 |
Shoes | 0.96 | 0.95 | 0.95 |
Watches | 0.99 | 0.98 | 0.98 |
In this project, i explored different CNN architectures for classifying product images into five categories. The ResNet50 model achieved the highest accuracy. Further improvements can be made by tuning hyperparameters, augmenting data...