Pre-trained models (ResNet, VGG, EfficientNet) in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the main advantage of using pre-trained models

Why do we often use pre-trained models like ResNet, VGG, or EfficientNet instead of training a model from scratch?

A They always require more data and time to train than new models.

B They allow faster training and better performance by using knowledge from large datasets.

C They are simpler models with fewer layers, so easier to understand.

D They only work for black and white images, not color images.

💡 Hint

Think about how learning from previous experience helps you solve new problems faster.

Pre-trained models have already learned useful features from large datasets like ImageNet. This helps new tasks train faster and often with better accuracy.

❓ Predict Output

intermediate

Output shape after passing an image through a pre-trained ResNet

Given a batch of 8 RGB images of size 224x224 passed through a pre-trained ResNet50 model (without the final classification layer), what is the shape of the output tensor?

Computer Vision

import torch
import torchvision.models as models

model = models.resnet50(pretrained=True)
model = torch.nn.Sequential(*list(model.children())[:-1])  # Remove final layer

input_tensor = torch.randn(8, 3, 224, 224)
output = model(input_tensor)
output_shape = output.shape

A (8, 512, 7, 7)

B (8, 1000)

C (8, 3, 224, 224)

D (8, 2048, 1, 1)

💡 Hint

Check the last convolutional layer output channels and spatial dimensions after pooling.

ResNet50 outputs a feature map with 2048 channels and spatial size 1x1 after global average pooling when the final classification layer is removed.

❓ Model Choice

advanced

Choosing the best pre-trained model for mobile deployment

You want to deploy an image classification model on a mobile device with limited memory and processing power. Which pre-trained model is the best choice?

A ResNet50 - medium size but heavier than EfficientNet

B VGG19 - very deep and large model

C EfficientNet - balances accuracy and efficiency

D ResNet152 - very deep with many layers

💡 Hint

Think about models designed for efficiency and smaller size.

EfficientNet models are designed to provide good accuracy with fewer parameters and less computation, making them suitable for mobile devices.

❓ Hyperparameter

advanced

Adjusting learning rate when fine-tuning a pre-trained model

When fine-tuning a pre-trained VGG16 model on a new dataset, which learning rate strategy is most appropriate?

A Use a very low learning rate (e.g., 0.0001) to avoid destroying learned features

B Use a very high learning rate (e.g., 0.1) to speed up training

C Use the same learning rate as training from scratch (e.g., 0.01)

D Use no learning rate because the model is pre-trained

💡 Hint

Think about how big changes affect already learned knowledge.

A low learning rate helps fine-tune the model gently without losing the useful features learned from the original dataset.

❓ Metrics

expert

Interpreting accuracy differences between pre-trained models

You fine-tune ResNet50 and EfficientNet-B0 on the same dataset. ResNet50 achieves 85% accuracy, EfficientNet-B0 achieves 88%. What is the most likely reason for this difference?

A EfficientNet-B0 has a better architecture that balances depth, width, and resolution.

B ResNet50 was trained with a higher learning rate causing overfitting.

C EfficientNet-B0 uses fewer parameters, so it always performs better.

D ResNet50 is older and cannot learn new datasets.

💡 Hint

Consider model design principles and efficiency.

EfficientNet uses a compound scaling method to optimize model size and accuracy, often outperforming older architectures like ResNet50.

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