Binary image classifier for classifying dog vs cats

Dataset:

Training samples: 20000 images (10000 per class)

Validation samples: 50000 images (2500 per class)

Testing samples: 12500 unlabelled images

Model architecture

PyTorch ConvNet architecture

(conv_1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(conv_2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)

(drop): Dropout(p=0.2, inplace=False)

(fc1): Linear(in_features=160000, out_features=512, bias=True)

(out): Linear(in_features=512, out_features=2, bias=True)

Trained model metrics

Training loss: 0.392

Validation loss: 0.40058

Test loss: 0.427

Test accuracy: 0.85