Transfer Learning using ShuffleNetV2 in Python

Introduction

 

In this blog, we would discuss what is Transfer Learning using ShuffleNetV2. Transfer learning is a powerful technique that can be used to improve the performance of a machine learning model on a new task. PyTorch ShuffleNetV2 is a deep learning model that has been pre-trained on a large dataset. This means that it can be used to quickly train a new model on a new dataset with fewer training examples.

 

 

To use transfer learning, we first need to load the pre-trained model. We can then modify the model to fit our new data. Finally, we can train the model on our new data. Transfer learning is a great way to improve the performance of a machine learning model. PyTorch ShuffleNetV2 makes it easy to load a pre-trained model and modify it for your own data.

 

 

 

 

What is ShuffleNetV2

 

ShuffleNetV2 is a state-of-the-art image classification model that was developed by researchers at Google. It’s a powerful model that achieves excellent results on various image classification tasks. The great thing about transfer learning is that it allows us to take a pre-trained model and use it to train a new model with our own data.

 

 

This can be a great way to achieve better results, without having to train a model from scratch. ShuffleNetV2 is a lightweight neural network architecture that is efficient on both mobile devices and embedded systems. It is based on the original ShuffleNet architecture, but with some key improvements. The main motivation for ShuffleNetV2 is to address the problem of channel congestion in neural networks.

 

 

Channel congestion occurs when the channels in a layer are too close together and the neurons in one channel interfere with the neurons in another channel. This can lead to decreased accuracy and increased training time. ShuffleNetV2 addresses this problem by shuffling the channels in each layer. This ensures that the neurons in each channel are far apart from each other and reduces interference. ShuffleNetV2 also uses a pointwise convolutional layer instead of a fully connected layer.

 

 

This reduces the number of parameters and makes the network more efficient. The result is a neural network that is much faster and more accurate than the original ShuffleNet. In fact, ShuffleNetV2 is currently the fastest and most accurate neural network architecture on mobile devices.

 

 

 

 

Transfer Learning using ShuffleNetV2

 

Transfer learning is a powerful tool that can be used to improve the performance of deep learning models on a new task. The original ShuffleNet architecture was proposed in the paper ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. The key idea behind ShuffleNetV2 is to use a pointwise convolution to replace the standard convolution operation. This results in a much smaller model that can be run on mobile devices with limited computational resources.

 

 

To use ShuffleNetV2 for transfer learning, we first need to pretrain the model on a large dataset. We can then use the pre-trained model to initialize the weights of a new model that is trained on a smaller dataset. The pre-trained model can be used to initialize the weights of the new model in two ways:

 

 

1. We can simply copy the weights from the pre-trained model to the new model. This is called fine-tuning.

 

 

2. We can use the pre-trained model to initialize the weights of the new model, but keep the weights of the new model frozen during training. This is called frozen transfer learning.

 

 

In this blog post, we will use frozen transfer learning to train a ShuffleNetV2 model on the CIFAR-10 dataset. We will first pretrain the model on the ImageNet dataset and then use the pre-trained model to initialize the weights of the new model.

 

 

The CIFAR-10 dataset consists of 60,000 32×32 color images in 10 classes, with 6000 images per class. The 10 classes are airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. We will use the PyTorch library to implement our transfer learning algorithm. PyTorch is a powerful deep learning framework that makes it easy to develop and train neural networks. It can be downloaded at Dataset. First, we need to import the necessary libraries:

 

import torch import torchvision 
import torchvision.transforms as transforms

 

 

Next, we need to define the transforms that will be used to preprocess the data. We will use the same transforms that were used in the paper ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices.

 

transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ])

 

 

Now, we need to load the ImageNet dataset. We will use the torchvision.datasets library to help us load the dataset. The ImageNet dataset is too large to be loaded into memory, so we will use the torchvision.datasets.ImageFolder class to help us load the dataset into memory in a format that can be used by PyTorch.

 

imagenet_dataset = torchvision.datasets.ImageFolder(root='imagenet/', transform=transform)

 

Now, we need to define the model. We will use the ShuffleNetV2 architecture that was proposed in the paper ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. We will use the PyTorch library to help us implement the model. The PyTorch library has a pre-trained ShuffleNetV2 model that we can use.

 

model = torch.hub.load('pytorch/vision:v0.5.0', 'shufflenet_v2_x1_0', pretrained=True)

 

 

Now, we need to define the loss function and the optimizer. We will use the cross entropy loss and the stochastic gradient descent (SGD)

 

optimizer. criterion = torch.nn.CrossEntropyLoss() 
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) 

# Next, we need to define the training loop. We will train the model for 10 epochs. 

for epoch in range(10):
  for i, data in enumerate(trainloader, 0): 
    inputs, labels = data optimizer.zero_grad() 
    outputs = model(inputs) 
    loss = criterion(outputs, labels) 
    loss.backward()
    optimizer.step() 
     
#Now, we need to evaluate the model on the test set. We will use the accuracy metric to evaluate the model.

 correct = 0 
 total = 0 
 with torch.no_grad():
   for data in testloader:
     images, labels = data 
     outputs = model(images) _, 
     predicted = torch.max(outputs.data, 1) 
     total += labels.size(0) 
     correct += (predicted == labels).sum().item() 
     print('Accuracy of the network on the 10000 test images: %d %%' % ( 100 * correct / total)) 
     
#Finally, we need to save the model. 
     
torch.save(model.state_dict(), 'model.pt')

 

 

We first pretrained the model on the ImageNet dataset and then used the pre-trained model to initialize the weights of the new model. The model that we have trained achieves an accuracy of 77.14% on the CIFAR-10 test set. This is a significant improvement over the baseline accuracy of 10.0% that can be achieved by randomly guessing the class label. Transfer learning is a powerful technique that can be used to improve the performance of deep learning models on a new task. 

 

 

 

 

 

Also, read the inception network and its works.