Pre-trained models (ResNet, VGG, EfficientNet) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained ResNet50 model from torchvision.

Computer Vision

import torchvision.models as models
model = models.[1](pretrained=True)

Drag options to blanks, or click blank then click option'

Aefficientnet_b0

Bresnet50

Cvgg16

Dalexnet

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a model name that does not match ResNet50, like vgg16 or alexnet.

Forgetting to set pretrained=True to load pre-trained weights.

The resnet50 function loads the ResNet50 pre-trained model from torchvision.

2fill in blank

medium

Complete the code to replace the final classification layer of a VGG16 model for 10 classes.

Computer Vision

import torch.nn as nn
import torchvision.models as models
model = models.vgg16(pretrained=True)
num_ftrs = model.classifier[6].in_features
model.classifier[6] = nn.[1](num_ftrs, 10)

Drag options to blanks, or click blank then click option'

ADropout

BConv2d

CReLU

DLinear

Attempts:

3 left

💡 Hint

Common Mistakes

Using Conv2d or ReLU instead of Linear for the final layer.

Not matching the output features to the number of classes.

The final layer of VGG16 classifier is a Linear layer. We replace it with a new Linear layer with 10 outputs for 10 classes.

3fill in blank

hard

Fix the error in the code to load EfficientNet-B0 pre-trained model from torchvision.

Computer Vision

import torchvision.models as models
model = models.[1](weights=models.EfficientNet_B0_Weights.IMAGENET1K_V1)

Drag options to blanks, or click blank then click option'

Aefficientnet_b0

Befficientnet_b1

Cresnet50

Dvgg16

Attempts:

3 left

💡 Hint

Common Mistakes

Using resnet50 or vgg16 instead of efficientnet_b0.

Incorrect weights enum for the model.

The correct function to load EfficientNet-B0 is efficientnet_b0. The weights argument uses the correct weights enum.

4fill in blank

hard

Fill both blanks to freeze all parameters of a pre-trained ResNet18 model.

Computer Vision

import torchvision.models as models
model = models.resnet18(pretrained=True)
for param in model.[1]():
    param.[2] = False

Drag options to blanks, or click blank then click option'

Aparameters

Bchildren

Crequires_grad

Dgrad

Attempts:

3 left

💡 Hint

Common Mistakes

Using children() instead of parameters() to freeze layers.

Setting grad instead of requires_grad.

We loop over all parameters using parameters() and set requires_grad = False to freeze them.

5fill in blank

hard

Fill all three blanks to create a dictionary that maps model names to their pre-trained loading functions.

Computer Vision

import torchvision.models as models
model_loaders = {
    '[1]': models.[2],
    '[3]': models.vgg16,
}

Drag options to blanks, or click blank then click option'

Aresnet50

Cvgg16

Defficientnet_b0

Attempts:

3 left

💡 Hint

Common Mistakes

Using function names as keys instead of strings.

Mismatching keys and function names.

The dictionary keys are model names as strings. The values are the corresponding functions from torchvision.models.

Practice

1. Which of the following is a key advantage of using pre-trained models like ResNet, VGG, or EfficientNet in computer vision tasks?

easy

A. They reduce the size of the input images automatically.

B. They save training time by using knowledge from large datasets.

C. They only work for text data, not images.

D. They always require training from scratch for every new task.

Solution

1. Step 1: Understand what pre-trained models do

   Pre-trained models are trained on large datasets and learn useful features that can be reused.

2. Step 2: Identify the benefit in context

   Using these models saves time because you don't need to train from scratch for every new task.

3. Final Answer:

   They save training time by using knowledge from large datasets. -> Option B

4. Quick Check:

   Pre-trained models save time = D [OK]

Hint: Pre-trained means already trained on big data [OK]

Common Mistakes:

• Thinking pre-trained models need full retraining
• Confusing image and text data applicability
• Assuming input size changes automatically

2. Which of the following is the correct way to load a pre-trained ResNet model in PyTorch?

easy

A. model = torch.load('resnet50')

B. model = torchvision.load_resnet50()

C. model = torchvision.models.ResNet50(weights='imagenet')

D. model = torchvision.models.resnet50(pretrained=True)

Solution

1. Step 1: Recall PyTorch syntax for loading pre-trained models

   In PyTorch, pre-trained models are loaded via torchvision.models with pretrained=True argument.

2. Step 2: Check each option

   model = torchvision.models.resnet50(pretrained=True) uses correct function and argument. Others are incorrect or invalid syntax.

3. Final Answer:

   model = torchvision.models.resnet50(pretrained=True) -> Option D

4. Quick Check:

   PyTorch pre-trained flag = pretrained=True [OK]

Hint: Use pretrained=True in torchvision.models [OK]

Common Mistakes:

• Using torch.load for model architecture
• Wrong function names like load_resnet50
• Incorrect argument names like weights='imagenet'

3. Consider this PyTorch code snippet using a pre-trained VGG16 model:

import torchvision.models as models
model = models.vgg16(pretrained=True)
print(type(model.features))

What will be the output type of model.features?

medium

A. <class 'torch.nn.Linear'>

B. <class 'torch.nn.ModuleList'>

C. <class 'torch.nn.Sequential'>

D. <class 'torch.nn.Conv2d'>

Solution

1. Step 1: Understand VGG16 model structure in PyTorch

   VGG16's feature extractor is implemented as a torch.nn.Sequential container of layers.

2. Step 2: Identify the type of model.features

   model.features groups convolutional layers in a Sequential module, so its type is torch.nn.Sequential.

3. Final Answer:

   <class 'torch.nn.Sequential'> -> Option C

4. Quick Check:

   VGG features = Sequential container [OK]

Hint: VGG features are in Sequential container [OK]

Common Mistakes:

• Confusing Sequential with ModuleList
• Thinking features is a single layer like Linear or Conv2d
• Not knowing PyTorch container types

4. You try to fine-tune a pre-trained EfficientNet model but get an error: AttributeError: module 'torchvision.models' has no attribute 'efficientnet'. What is the most likely cause?

medium

A. Your torchvision version is outdated and does not include EfficientNet.

B. You forgot to import torch.

C. EfficientNet models are not available in PyTorch.

D. You need to set pretrained=True to access EfficientNet.

Solution

1. Step 1: Understand the error message

   The error says torchvision.models has no attribute 'efficientnet', meaning the function is missing.

2. Step 2: Check common causes

   EfficientNet was added in newer torchvision versions. An outdated version lacks it.

3. Final Answer:

   Your torchvision version is outdated and does not include EfficientNet. -> Option A

4. Quick Check:

   Missing attribute = outdated torchvision [OK]

Hint: Check torchvision version for model availability [OK]

Common Mistakes:

• Assuming import torch fixes model availability
• Thinking EfficientNet is not in PyTorch at all
• Confusing pretrained flag with missing attribute

5. You want to build an image classifier for a small dataset with limited computing power. Which pre-trained model is the best choice to balance accuracy and efficiency?

hard

A. EfficientNet, because it scales well and is efficient for small data.

B. VGG16, because it is simple but very large and slow.

C. ResNet50, because it is very deep and accurate but heavy.

D. Train a new model from scratch for best results.

Solution

1. Step 1: Consider dataset size and computing power

   Small data and limited power require efficient models to avoid overfitting and long training.

2. Step 2: Compare model characteristics

   ResNet50 is accurate but heavy; VGG16 is large and slow; EfficientNet is designed for efficiency and good accuracy.

3. Step 3: Choose the best fit

   EfficientNet balances accuracy and efficiency, making it ideal for small datasets and limited resources.

4. Final Answer:

   EfficientNet, because it scales well and is efficient for small data. -> Option A

5. Quick Check:

   Efficiency + accuracy = EfficientNet [OK]

Hint: EfficientNet balances speed and accuracy well [OK]

Common Mistakes:

• Choosing heavy models for small data
• Ignoring efficiency for limited computing power
• Thinking training from scratch is always better