Small dataset strategies in Computer Vision - Model Pipeline Trace

Model Pipeline - Small dataset strategies

This pipeline shows how to train a computer vision model when you have a small dataset. It uses data augmentation to create more images, then trains a simple neural network. This helps the model learn better despite limited data.

Data Flow - 4 Stages

1Original Dataset

100 images x 64x64 pixels x 3 color channels→Load small image dataset→100 images x 64x64 pixels x 3 color channels

Image of a cat, 64x64 pixels, RGB

↓

2Data Augmentation

100 images x 64x64 pixels x 3 channels→Apply random flips, rotations, zooms to increase data→500 images x 64x64 pixels x 3 channels

Original cat image flipped horizontally

↓

3Train/Test Split

500 images x 64x64 pixels x 3 channels→Split data into 80% train and 20% test→400 train images, 100 test images

400 augmented cat/dog images for training

↓

4Model Training

400 images x 64x64 pixels x 3 channels→Train CNN model with augmented data→Trained model

CNN learns to classify cats vs dogs

Training Trace - Epoch by Epoch

Loss
1.2 |*         
1.0 | *        
0.8 |  *       
0.6 |   *      
0.4 |    *     
0.2 |     *    
0.0 +----------
      1 3 5 7 10 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning, accuracy low
3	0.8	0.65	Loss decreases, accuracy improves
5	0.5	0.80	Model learns important features
7	0.35	0.88	Good convergence with augmented data
10	0.25	0.92	Model achieves high accuracy

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Convolutional Layer

Layer 3: Max Pooling

Layer 4: Flatten

Layer 5: Dense Layer with Softmax

Model Quiz - 3 Questions

Why do we use data augmentation in small dataset training?

A To create more varied training images

B To reduce the image size

C To remove noisy images

D To speed up training

Key Insight

Using data augmentation helps the model see more varied images, which improves learning and accuracy when the original dataset is small.

Practice

1. Which of the following is a common strategy to improve model performance when you have a small image dataset?

easy

A. Train a deep model from scratch without any pre-trained weights

B. Use data augmentation to create more training images

C. Ignore validation to use all data for training

D. Reduce image resolution to save memory only

Solution

1. Step 1: Understand small dataset challenges

   Small datasets often cause models to overfit and perform poorly on new data.

2. Step 2: Identify effective strategies

   Data augmentation creates new images by modifying existing ones, increasing data variety and helping the model generalize better.

3. Final Answer:

   Use data augmentation to create more training images -> Option B

4. Quick Check:

   Data augmentation = More data variety [OK]

Hint: More data variety helps small datasets [OK]

Common Mistakes:

• Training from scratch causes overfitting
• Ignoring validation hides model issues
• Reducing resolution alone doesn't add data

2. Which code snippet correctly applies data augmentation using the Python library torchvision.transforms?

easy

A. transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor()])

B. transforms.RandomCrop(32, 32)

C. transforms.ToTensor(), transforms.Normalize()

D. transforms.Resize(256)

Solution

1. Step 1: Recognize data augmentation syntax

   Data augmentation requires combining multiple transforms, usually with Compose.

2. Step 2: Check which option uses Compose with augmentation

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor()]) uses Compose with RandomHorizontalFlip (augmentation) and ToTensor (conversion), which is correct.

3. Final Answer:

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor()]) -> Option A

4. Quick Check:

   Compose + augmentation = transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor()]) [OK]

Hint: Use Compose to combine augmentations [OK]

Common Mistakes:

• Using single transform without Compose
• Missing ToTensor conversion
• Using only resizing without augmentation

3. Consider this Python code using transfer learning with PyTorch:

import torchvision.models as models
model = models.resnet18(pretrained=True)
for param in model.parameters():
    param.requires_grad = False
model.fc = torch.nn.Linear(512, 2)

What does this code do?

medium

A. Trains all layers of ResNet18 from scratch

B. Unfreezes all layers for fine-tuning

C. Freezes all layers except the last fully connected layer

D. Removes the last layer without replacement

Solution

1. Step 1: Analyze parameter freezing

   The loop sets requires_grad=False for all parameters, freezing them during training.

2. Step 2: Check the last layer replacement

   The last fully connected layer (fc) is replaced with a new Linear layer, which by default has requires_grad=True.

3. Final Answer:

   Freezes all layers except the last fully connected layer -> Option C

4. Quick Check:

   Freeze all but last layer = Freezes all layers except the last fully connected layer [OK]

Hint: Freeze parameters, then replace last layer [OK]

Common Mistakes:

• Assuming all layers are trainable
• Not noticing last layer replacement
• Confusing freezing with unfreezing

4. You wrote this code to augment images but get an error:

transform = transforms.Compose([
    transforms.RandomRotation(30),
    transforms.ToTensor
])

What is the error and how to fix it?

medium

A. Transforms must be applied outside Compose

B. RandomRotation requires degrees as a tuple, fix by using (0,30)

C. Compose should be replaced by Sequential

D. Missing parentheses after ToTensor; fix by using transforms.ToTensor()

Solution

1. Step 1: Identify the error in ToTensor usage

   transforms.ToTensor is a class, missing parentheses means it's not called, causing an error.

2. Step 2: Correct the syntax

   Add parentheses to call ToTensor: transforms.ToTensor()

3. Final Answer:

   Missing parentheses after ToTensor; fix by using transforms.ToTensor() -> Option D

4. Quick Check:

   Call ToTensor() as function [OK]

Hint: Call transform classes with () [OK]

Common Mistakes:

• Forgetting parentheses on transform classes
• Misusing Compose with wrong functions
• Incorrect argument types for RandomRotation

5. You have only 100 labeled images for a classification task. Which combined approach best improves model accuracy?

hard

A. Use transfer learning with a pre-trained model and apply data augmentation

B. Train a deep CNN from scratch with no augmentation

C. Use only data augmentation without pre-trained weights

D. Increase batch size to 512 and train for fewer epochs

Solution

1. Step 1: Understand small dataset limits

   With only 100 images, training deep models from scratch risks overfitting and poor generalization.

2. Step 2: Combine transfer learning and augmentation

   Transfer learning uses knowledge from large datasets, and augmentation increases data variety, both improving accuracy.

3. Final Answer:

   Use transfer learning with a pre-trained model and apply data augmentation -> Option A

4. Quick Check:

   Transfer learning + augmentation = Best for small data [OK]

Hint: Combine pre-trained models with augmentation [OK]

Common Mistakes:

• Training from scratch with little data
• Relying on augmentation alone
• Using too large batch size causing poor learning