Custom transforms in PyTorch - Model Pipeline Trace

Model Pipeline - Custom transforms

This pipeline shows how custom data transforms prepare images for a PyTorch model. The transforms clean and change images before training, helping the model learn better.

Data Flow - 4 Stages

1Raw Image Data

1000 images x 3 channels x 256 height x 256 width→Original images loaded from disk→1000 images x 3 channels x 256 height x 256 width

An image of a cat with size 256x256 pixels and 3 color channels

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2Custom Transform: Resize and Normalize

1000 images x 3 channels x 256 height x 256 width→Resize images to 128x128 and normalize pixel values to 0-1 range→1000 images x 3 channels x 128 height x 128 width

Resized cat image now 128x128 pixels with pixel values scaled between 0 and 1

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3Custom Transform: Random Horizontal Flip

1000 images x 3 channels x 128 height x 128 width→Randomly flip images horizontally with 50% chance→1000 images x 3 channels x 128 height x 128 width

Some cat images flipped left to right, others unchanged

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4Tensor Conversion

1000 images x 3 channels x 128 height x 128 width→Convert images from numpy arrays to PyTorch tensors→1000 tensors x 3 channels x 128 height x 128 width

Cat image now a PyTorch tensor ready for model input

Training Trace - Epoch by Epoch

Loss
1.2 |****
0.9 |***
0.7 |**
0.55|*
0.45| 
Epochs -> 1 2 3 4 5

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning with high loss and low accuracy
2	0.9	0.60	Loss decreases and accuracy improves as model learns
3	0.7	0.72	Model continues to improve with better predictions
4	0.55	0.80	Loss lowers further, accuracy nearing good performance
5	0.45	0.85	Model converges with low loss and high accuracy

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Normalize Pixels

Layer 3: Random Horizontal Flip

Layer 4: Convert to Tensor

Layer 5: Model Forward Pass

Model Quiz - 3 Questions

Why do we resize images in the custom transform?

A To increase image size for better detail

B To reduce image size and speed up training

C To change image colors

D To convert images to grayscale

Key Insight

Custom transforms prepare raw images by resizing, normalizing, and augmenting them. This helps the model learn better by giving consistent and varied data, leading to improved accuracy and faster training.