Here we train the VGG16 model and evaluate its performance on the test image set.
The availability of datasets like DeepFashion open up new possibilities for the fashion industry. In this series of articles, we’ll showcase an AI-powered deep learning system that can revolutionize the fashion design industry by helping us better understand customers’ needs.
In this project, we’ll use:
- Jupyter Notebook as the IDE
- A custom subset of the DeepFashion dataset — relatively small to reduce the computational and memory overhead
We are assuming that you are familiar with the concepts of deep learning, as well as with Jupyter Notebooks and TensorFlow. If you’re new to Jupyter Notebooks, start with this tutorial. You are welcome to download the project code.
In the previous article, we showed you how to load the DeepFashion dataset, and how to restructure the VGG16 model to fit our clothing classification task. In this article, we’ll train VGG16 to classify 15 different clothing categories and evaluate the model performance.
Transfer learning for VGG16 starts with freezing the model weights that had been obtained by training the model on a huge dataset such as ImageNet. These learned weights and filters provide the network with great feature extraction capabilities, which will help us boost its performance when it is trained to classify clothing categories. Hence, only the Fully Connected (FC) layers will be trained while keeping the feature extraction part of the model almost frozen (by setting a very low learning rate, like 0.001). Let’s freeze the feature extraction layers by setting them to False:
for layer in conv_model.layers:
layer.trainable = False
Now, we can compile our model while selecting the learning rate (0.001) and optimizer (Adamax):
After compiling, we can start model training using the
fit_generator function, since we used
ImageDataGenerator to load our data. We will train and validate our network using data indicated as
val_dataset, respectively. We’ll train with three epochs, but this number can be increased depending on the network performance.
history = full_model.fit_generator(
validation_data = val_dataset,
Running the above code will result in the following output:
Now, to plot the learning and loss curves for the network, let’s add the
def plot_history(history, yrange):
'''Plot loss and accuracy as a function of the epoch,
for the training and validation datasets.
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.title('Training and validation accuracy')
plt.title('Training and validation loss')
This function will generate these two plots:
Evaluating VGG16 on New Images
Our network performed well during training. So it should also perform well when tested on images of clothes that it had not seen before, right? We’ll test it on our testing set of images.
First, let’s load the test set, and then pass the test images to the model using the
model.evaluate function to measure the network accuracy.
from tensorflow.keras.preprocessing.image import ImageDataGenerator
test_datagen = ImageDataGenerator()
test_generator = test_datagen.flow_from_directory(test_dir, target_size=(224, 224), batch_size=3, class_mode='categorical')
Testresults = full_model.evaluate(test_generator)
print("test loss, test acc:", Testresults)
Well, it is clear that our network is well trained. With no overfitting: it had achieved an accuracy of 92% on the testing set.
In the next article, we’ll evaluate VGG19 using real images taken by a phone camera. Stay tuned!
Dr. Helwan is a machine learning and medical image analysis enthusiast.
His research interests include but not limited to Machine and deep learning in medicine, Medical computational intelligence, Biomedical image processing, and Biomedical engineering and systems.