Load torchvision Models
Use pretrained architectures in one call.
Load torchvision Models is a free Deep Learning Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Reinvent It?
You rarely need to build a famous net from scratch. PyTorchs torchvision library ships them ready to use.
A Zoo of Models
From ResNet to VGG to modern nets, torchvision.models is a library of proven architectures at your fingertips.
Load One in a Line
Grabbing an architecture is a single call. Here you get a ResNet-18 with random, untrained weights.
from torchvision import models
net = models.resnet18()Bring the Pretrained Weights
The real power is loading pretrained weights learned on ImageNet, so the model already knows useful visual features.
from torchvision.models import resnet18, ResNet18_Weights
net = resnet18(weights=ResNet18_Weights.DEFAULT)A Head Start
Those weights are a huge head start. You can fine-tune them on your own task with far less data.
Match the Preprocessing
Pretrained models expect specific input sizes and normalization. The weights object carries the exact transforms to apply.
weights = ResNet18_Weights.DEFAULT
preprocess = weights.transforms()Set Eval Mode
Before predicting, call eval so layers like batch norm and dropout behave correctly for inference.
net.eval()
# disables dropout, freezes batch-norm statsSwap the Final Layer
ImageNet has 1000 classes. To reuse the net, replace its final fully connected layer to match your classes.
import torch.nn as nn
net.fc = nn.Linear(net.fc.in_features, 10)Freeze to Save Effort
For quick transfer learning, freeze the backbone so only your new head trains, which is fast and data-light.
for p in net.parameters():
p.requires_grad = FalseRead the Class Names
The weights metadata even lists the human-readable categories, so you can turn an index back into a label.
labels = ResNet18_Weights.DEFAULT.meta["categories"]Stand on Giants
With one import you reuse years of research and compute. This is the everyday workflow of applied deep learning.
Quick Check
Recall how to load a model that already knows ImageNet.
Recap: Reuse the Best
With torchvision you load proven architectures and pretrained weights in a line, then adapt them to your task. You are ready to build!
Frequently asked questions
Is the “Load torchvision Models” lesson free?
Yes — the full text of “Load torchvision Models” is free to read here on the web, and the Deep Learning Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “Load torchvision Models”?
Use pretrained architectures in one call. You practise Deep Learning Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Load torchvision Models” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Deep Learning Academy lesson?
Yes. Every Deep Learning Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- LeNet & AlexNet: The First Wins
- VGG: Stacks of Small Filters
- ResNet: Skip Connections Go Deep
- Load torchvision Models