By Hiren Rupchandani, Abhinav Jangir, and Ashish Lepcha
In our previous article, we saw how to preprocess our image data using several different techniques. Now it is time to build a model using this pre-processed data. So, let’s get started:
The Dataset
We are going to classify whether a person is wearing a mask or not based on the input image which is the face of a person. The dataset contains two types of images — People wearing a face mask and people not wearing a face mask. Let’s take a glimpse of images in both the classes:
Data Pre-processing
Data Augmentation
- We are doing data augmentation by randomly applying various features.
- This increases the diversity of data available for training models, without actually collecting new data
- Some of the common Data Augmentation filters are :
- Random Rotation
- Horizontal Shift
- Vertical Shift
- Random Fliping
- Shearing
- Random Zooming
Model Building
- Our Model consists of 3 convolutional layers followed by Max Pooling Layers and dropout.
- There is a fully connected layer with 128 units after convolutional that is activated by a ReLU activation function.
- Here we are using Binary Cross-Entropy loss with an ADAM optimizer for our binary classification problem.
- According to the model summary, there are 6,446,369 trainable parameters.
- The training/validation loss and accuracy graphs for the model is as follows:
Using Transfer Learning
- Another way to build a model can be using transfer learning. But what is it? It is like learning to ride a bicycle and taking that experience to learn a bike/scooter.
- Transfer learning consists of taking features learned on one problem and leveraging them on a new, similar problem.
- For instance, features from a model that has learned to identify raccoons may be useful to kick-start a model meant to identify tanukis.
- Transfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch.
- The most common incarnation of transfer learning in the context of deep learning is the following workflow:
- Take layers from a previously trained model.
- Freeze them, so as to avoid destroying any of the information they contain during future training rounds.
- Add some new, trainable layers on top of the frozen layers. They will learn to turn the old features into predictions on a new dataset.
- Train the new layers on your dataset. - A last, optional step, is fine-tuning, which consists of unfreezing the entire model you obtained above (or part of it) and re-training it on the new data with a very low learning rate.
- This can potentially achieve meaningful improvements, by incrementally adapting the pretrained features to the new data.
MobileNet
- MobileNet-v2 is a convolutional neural network that is 53 layers deep.
- You can load a pretrained version of the network trained on more than a million images from the ImageNet database.
- The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.
- As a result, the network has learned rich feature representations for a wide range of images.
- The network has an image input size of 224-by-224.
- MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases.
- The architecture delivers high accuracy results while keeping the parameters and mathematical operations as low as possible to bring deep neural networks to mobile devices.
- In MobileNetV2, there are two types of blocks.
- Inverted Residual Block
- Bottleneck Residual Block - There are 3 layers for both types of blocks.
- One is a residual block with a stride of 1. Another one is a block with a stride of 2 for downsizing.
- There are two types of Convolution layers in MobileNet V2 architecture:
- 1x1 Convolution
- 3x3 Depthwise Convolution - A MobileNetV2 network looks like this:
- We have connected the output of a base MobileNetV2 network with a new model.
- This model consists of an average pooling layer, followed by a flattening layer, and finally a fully connected dense neural network.
- The output layer consists of a sigmoid activation to perform the binary classification.
- On training this network, we are achieving an accuracy of 0.99.
- If we were to fine-tune the model by re-training the entire model on our data, we can even achieve a test accuracy of 1.0.
- You can visualize the model’s training and validation accuracy and loss using this TensorBoard Extension.
- You can observe from the graphs that for our model we got a validation accuracy of around 88% but by using transfer learning, we achieved a validation accuracy of more than 97%.
- There we have it — a model ready to be deployed, with great accuracy.
What’s Next?
In the next article of this series, we will deploy our model using Flask.
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