Using Kaggle Dogs vs Cats to experiment with different Image Classification architectures
This repository contains a set of experiments performed on Dogs vs Cats image dataset downloaded from https://www.kaggle.com/c/dogs-vs-cats/data.
Hardware Specifications
| CPU | Intel Core i5 9th Gen |
| GPU | Nvidia GTX 1650 4GB (Mobile) |
| RAM | 8GB 2700Hz Dual channel |
| Storage | M.2 SSD 250GB |
The models were built and trained using Keras and Tensorflow GPU
| Model | Parameters | Val Accuracy | Val Loss | Notes |
|---|---|---|---|---|
| LeNet5 | 1,311,246 | 74.17 | 0.2941 | Learning rate not set, Dropout rate not set, Batch Normalisation not set |
| AlexNet | 58,289,538 | 87.61 | 0.1535 | Learning rate not set, Dropout rate not set, Batch Normalisation not set |
| VGG-16 | 134,268,738 | N/A | N/A | Training not started because of insufficent memory on the graphic card |
Future models
- VGG-13
- GoogLeNet/Inception
- ResNET50
- ResNET100