Sudden spike in validation loss

Question

So I am doing binary image classification on a small data set containing 250 images in each class, I am using transfer learning using Resnet50 as base network architecture and over it I've added 2 hidden layer and one final output layer, after training for 20 epochs, what I've saw is that loss is suddenly increases in initial epoch, I am unable to understand the reason behind it.

Network architecture -

image_input = Input(shape=(224, 224, 3))
model = ResNet50(input_tensor=image_input,include_top=True, weights='imagenet')
last_layer = model.get_layer('avg_pool').output
x = Flatten(name='flatten')(last_layer)
x = Dense(1000, activation='relu', name='fc1000')(x)
x = Dropout(0.5)(x)
x = Dense(200, activation='relu', name='fc200')(x)
x = Dropout(0.5)(x)
out = Dense(num_classes, activation='softmax', name='output')(x)
custom_model = Model(image_input, out)

I am using binary_crossentropy, Adam with default parameters

Loss -

Accuracy -

Answer 1

With such small class of data, there is definitely chance of overfitting do increase your dataset size and check it out use data augmentation if possible