Why is this ResNet50 misclassifying objects?

Question

I'm new to Deep Learning, and I have some conceptual problems. I followed a simple tutorial here, and trained a model in Keras to do image classification on 10 classes of logos. I prepared 10 classes with each class having almost 100 images. My trained Resnet50 model performs exceptionally great when the image is one of those 10 logos, with 1.00 probability. But the problem is if I pass a non-logo item, a random image totally unrelated visually, still it marks it as one of those logos with close to 1.00 probability!

I'm confused. Am I missing anything? Why is this happening? How to find a solution? I need to find logos in video frames. But right now, with a high possbility each frame is marked as a logo!

Here is my simple training code:

def build_finetune_model(base_model, dropout, fc_layers, num_classes):
    for layer in base_model.layers:
        layer.trainable = False

    x = base_model.output
    x = Flatten()(x)
    for fc in fc_layers:
        # New FC layer, random init
        x = Dense(fc, activation='relu')(x) 
        x = Dropout(dropout)(x)

    # New softmax layer
    predictions = Dense(num_classes, activation='softmax')(x) 
    finetune_model = Model(inputs=base_model.input, outputs=predictions)
    return finetune_model
finetune_model = build_finetune_model(base_model, dropout=dropout, fc_layers=FC_LAYERS, num_classes=len(class_list))
adam = Adam(lr=0.00001)
finetune_model.compile(adam, loss='categorical_crossentropy', metrics=['accuracy'])
filepath="./checkpoints/" + "ResNet50" + "_model_weights.h5"
checkpoint = ModelCheckpoint(filepath, monitor=["acc"], verbose=1, mode='max')
callbacks_list = [checkpoint]

history = finetune_model.fit_generator(train_generator, epochs=NUM_EPOCHS, workers=8, 
                                       steps_per_epoch=steps_per_epoch, 
                                       shuffle=True, callbacks=callbacks_list)

plot_training(history)

Answer 1

Your problem is a classification problem. If you are following the tutorial, it is using the ResNet50 network, which is a convolutional neural network with one fully connected layer at the end. At the end the activation function is softmax. Detailed description of the activation function can be found here: Softmax function explained

Basically, softmax increases the difference between the higher probability and lower probability. It also limits the output between 0 and 1.

Problem Origin

Due to the nature of the softmax function, it always chooses the best one and enlarge the value to be a value near one, even if the range of the output predictions is very small like 0-0.1. Also you training data only have the data of the 10 logos labelled, so if the network see unseen images with no logo it recognizes, it predicts the one with the most similarity. If you want to classify images with no logos in it, you should add an extra class in the training dataset and also the code to train the network to learn to classify images which is not in the 5 class into a separate maybe called unlabeled. Hope I can help you and have a nice day.