PyTorch is a modular, flexible, open-source deep learning framework used for applications such as computer vision and natural language processing. Developed by Meta AI, PyTorch is among one of the most popular deep learning frameworks, alongside TensorFlow.
Unlike TensorFlow, however, PyTorch uses a dynamic computation graph rather than a static computation graph. This means you have the ability to define, change, and execute nodes at runtime, offering greater flexibility during model development and making debugging easier, which is ideal for developers and researchers in creating the perfect model for their needs.
PyTorch supports the entire machine learning lifecycle from model building and training to deployment and performance optimization. It offers native support for GPU acceleration and seamless integration with the Python data science stack, making it ideal for fast experimentation and production-level applications.
Why PyTorch for Computer Vision?
Computer vision is one of the most impactful and popular domains in AI spanning use cases like autonomous vehicles, facial recognition, medical imaging, augmented reality, and industrial automation.
A key part of this ecosystem is the TorchVision library, which provides easy access to state-of-the-art pretrained models like ResNet, along with powerful tools for image datasets, data augmentation, and model training. PyTorch also offers seamless GPU acceleration and flexible deployment options via TorchScript and ONNX, making it easy to optimize and deploy models across diverse platforms.
Key PyTorch Concepts
Tensors
In PyTorch, tensors are multi-dimensional arrays similar to NumPy arrays, but with GPU acceleration.
Example:
import torch
# Create a 2x3 tensor
x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
print(x.shape) # torch.Size([2, 3])Autograd
PyTorch’s autograd engine tracks operations on tensors and automatically computes gradients during backpropagation. It tracks all operations on tensors with requires_grad=True.
Example:
import torch
# Step 1: Create a tensor with requires_grad=True so autograd can track it
x = torch.tensor([4.0], requires_grad=True)
# Step 2: Define the function y = x^3 + 2x
y = x ** 3 + 2 * x
# Step 3: Compute the gradient dy/dx using backward()
y.backward()
# Step 4: Print the gradient (dy/dx at x=4)
print(f"x: {x.item()}") # x: 4.0
print(f"y: {y.item()}") # y: 72.0
print(f"dy/dx: {x.grad.item()}") # dy/dx: 50.0
# dy/dx = 3x^2 + 2 = 3*16 + 2 = 50
Neural Networks with torch.nn
Neural networks in PyTorch are created using the torch.nn module. The fundamental building block is the nn.Module class, which can be extended to define custom models.
Example:
import torch.nn as nn
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.linear = nn.Linear(10, 5)
def forward(self, x):
return self.linear(x)How to Build in PyTorch
Set Up Your Environment
PyTorch is available in many environments, but many prefer Google Colab because it provides cloud-based compute power and avoids the limitations of local hardware.A Simple Image Classifier using CNNs
Let’s build a CNN (convolutional neural network) that classifies images from the banana ripeness dataset, which includes 13478 color images across 4 classes. Our goal is to categorize each image into overripe, ripe, rotten, and unripe. First, we need to install and import the dependencies. First, we need to install and import the dependencies.
!pip install torch torchvision roboflowimport os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from roboflow import Roboflow
from torchvision import datasets, transforms
from torch.utils.data import DataLoaderNext, we need to load the banana ripeness dataset from Roboflow into training and testing sets, applying transformations along the way. After creating an account and signing in, you can find your API key by going to Settings, Workspaces, then API keys. One advantage of Roboflow is easy access to a wide variety of datasets.
After importing the dataset, we normalize all the images. The transforms.Compose function creates a pipeline of transformations applied to each image. In this pipeline, we convert each image into a PyTorch tensor and normalize the tensor values.
rf = Roboflow(api_key="YOUR_API_KEY_HERE")
project = rf.workspace("roboflow-universe-projects").project("banana-ripeness-classification")
dataset = project.version(5).download("folder")
train_dir = os.path.join("Banana-Ripeness-Classification-5", "train")
valid_dir = os.path.join("Banana-Ripeness-Classification-5", "valid")
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
train_dataset = datasets.ImageFolder(root=train_dir, transform=transform)
valid_dataset = datasets.ImageFolder(root=valid_dir, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
valid_loader = DataLoader(valid_dataset, batch_size=32, shuffle=False)Once the data is prepared, we can create our custom CNN. CNNs are powerful models for image classification, which consists of two convolutional layers, one pooling layer, one fully connected layer, and an output layer as defined below. The forward() method defines how the input data moves through the network. Below is how we defined our custom CNN.
Define the CNN
class BananaCNN(nn.Module):
def __init__(self, num_classes):
super(BananaCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
self.fc1 = nn.Linear(32 * 56 * 56, 128)
self.fc2 = nn.Linear(128, num_classes)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 32 * 56 * 56)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return xSet Device, Loss Function, and Optimizer
Now that the model is built, we can prepare it for training. We use the CUDA if it is available for faster processing; otherwise, defaulting to the CPU, Adam optimizer, and the cross entropy loss function, which is suitable for classification tasks.
num_classes = len(train_dataset.classes)
model = BananaCNN(num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)Train the Model
We will train the model for 10 epochs. In each epoch, we start by zeroing out the gradients from the previous iteration to prevent accumulation. Then, for each batch of images, we perform a forward pass, compute the loss, backpropagate the error, and update the model’s weights. Finally, we track and print the average loss for each epoch to monitor training progress.
for epoch in range(10):
model.train()
running_loss = 0.0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
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):.4f}")Evaluate the Model
After training is complete, we evaluate the model’s performance on the test set. We set the model to evaluation mode with model.eval(), which disables dropout and batch normalization layers if present. Using torch.no_grad() ensures that no gradients are computed during evaluation, which saves memory and speeds up computation. We then calculate the total number of correct predictions and compute the accuracy percentage.
model.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in valid_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f"Validation Accuracy: {100 * correct / total:.2f}%")Business Impact of PyTorch
PyTorch is the standard in AI development such as computer vision, natural language processing, and generative AI. It is the framework of choice for researchers because of its support for rapid experimentation, flexibility, and developer-friendly design. Leveraging PyTorch empowers teams to move seamlessly from research to production.
In contrast to the static computation graph used by TensorFlow, PyTorch uses a dynamic computation graph that allows for faster development cycles, reducing R&D and engineering costs. Furthermore, PyTorch has native support for GPUs with CUDA, as well as TPU integrations and ONNX for cross-platform deployment. Additionally, endpoint deployment is made easy on Roboflow, which supports many of the popular models today including Resnet and YOLOv8.
PyTorch’s strengths in rapid experimentation and development leads to building new and better AI features faster than ever, ultimately increasing revenue. With access to models like EfficientNet and DETR, PyTorch helps teams build smarter vision pipelines faster.
PyTorch Resources and Next Steps
PyTorch Official Docs
PyTorch Courses
PyTorch GitHub Repositories
Start Prototyping, Start Scaling with PyTorch
PyTorch provides full control with dynamic computation. Its ability to define, modify, and execute nodes at runtime offers greater flexibility during model development and allows debugging to be easier. This makes PyTorch the ideal framework for faster research and development and leading into faster production with the perfect model for your needs.
PyTorch is powering production AI at scale with its main advantage of rapid experimentation. This in turn allows you to create the perfect AI solutions that align with your business goals efficiently while reducing costs and increasing revenue.
Whether you’re building your first image classifier or deploying advanced vision systems at scale, PyTorch gives you the speed, flexibility, and ecosystem to do it right.
Building with PyTorch? Roboflow lets you seamlessly generate custom datasets, preprocess images, and export directly to PyTorch. Start your next PyTorch project faster with Roboflow for free.
Cite this Post
Use the following entry to cite this post in your research:
Contributing Writer. (Jun 9, 2025). The Ultimate Guide to PyTorch for Computer Vision. Roboflow Blog: https://blog.roboflow.com/pytorch-computer-vision/