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.