PyTorch, an open-source machine learning framework, has been gaining popularity for its simplicity and ease of use in deep learning applications. Fashion industry experts can utilize PyTorch to identify trends and styles, recommend clothing combinations, and even predict the success of styles in upcoming seasons. In this article, we will share how to implement a simple fashion analysis using PyTorch.
Problem Definition
Our objective is to analyze a dataset of images, each representing various clothing items, and use PyTorch to build a deep learning model that can categorize these images into different styles and trends. By doing so, we aim to identify prominent trends in the fashion industry and combinations of garments that work well together.
Setting up the Environment
To get started, we will need to have Python and PyTorch installed on our system. Additionally, we will use torchvision, a package containing popular datasets, model architectures, and common image transformations for computer vision.
pip install torch torchvision
With these packages installed, we can now start tackling the problem.
Dataset and DataLoader
In this article, we will use the FashionMNIST dataset, which is a popular dataset for image classification and available through torchvision. The dataset contains 60,000 training images and 10,000 test images, with each image representing clothing items of the 10 different classes.
from torchvision import datasets, transforms
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
train_data = datasets.FashionMNIST(root="./data", train=True, download=True, transform=transform)
test_data = datasets.FashionMNIST(root="./data", train=False, download=True, transform=transform)
We also need to set up DataLoaders to efficiently load and preprocess the dataset images.
from torch.utils.data import DataLoader train_loader = DataLoader(train_data, batch_size=64, shuffle=True) test_loader = DataLoader(test_data, batch_size=64, shuffle=False)
Model Architecture
Next, we will define our deep learning model, which will be a Convolutional Neural Network (CNN). A CNN is suitable for analyzing images, as it can learn complex patterns and structures within the fashion data.
import torch.nn as nn
import torch.nn.functional as F
class FashionModel(nn.Module):
def __init__(self):
super(FashionModel, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(64*7*7, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 64*7*7)
x = F.relu(self.fc1(x))
x = F.log_softmax(self.fc2(x), dim=1)
return x
model = FashionModel()
Training and Evaluation
Now, we will train the model using Cross-Entropy Loss and Stochastic Gradient Descent (SGD) as our optimizer. We’ll then evaluate the model on the test dataset to check its performance.
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
epochs = 10
for epoch in range(epochs):
running_loss = 0.0
for inputs, labels in train_loader:
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f"Epoch {epoch + 1}, Loss: {running_loss / len(train_loader)}")
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in test_loader:
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f"Accuracy: {correct / total * 100}%")
With this model and training procedure, we can achieve an impressive accuracy on the test dataset, enabling us to recognize and categorize the various styles and trends in the fashion industry.
