An Keras + Python image classifier that determines whether or not an image is of a cat.
rpeden / cat-or-not Goto Github PK
View Code? Open in Web Editor NEWIs it a cat, or something else?
License: BSD 3-Clause "New" or "Revised" License
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Question regarding retrain.py
Hi @rpeden, thank you for this awesome tutorial.
I have one question though: retrain.py defines load_training_data() function, but it's never get called. What is its purpose?
Changing the architecture?
Very useful tutorial. Thanks.
I'm new to deep learning, but my target application is fire detection which I guess has differences vs cat detection. What changes would you make on your architecture for a better accuracy? Currently if fire is like full screen, it detects it. If smaller, it fails.
First thing is changing grayscale to colored version. Or maybe adding a new layer/bigger kernel size. Any helps or hints?
def create_model():
model = Sequential()
model.add(Conv2D(32, kernel_size = (3, 3), activation='relu', input_shape=(IMAGE_SIZE, IMAGE_SIZE, 1)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())
model.add(Conv2D(128, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())
model.add(Conv2D(256, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(128, activation='relu'))
model.add(Dense(2, activation = 'softmax'))
return model
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