Model Pipeline - Image dataset from folders
This pipeline loads images organized in folders by class, prepares them for training a model, trains a simple image classifier, and shows how predictions are made.
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Image dataset from folders in PyTorch - Model Pipeline Trace
Data Flow - 5 Stages
1Load images from folders
1000 images organized in 5 folders→Read images and assign labels based on folder names→1000 samples x (3 color channels x 224 x 224 pixels)
Folder 'cats' contains 200 images labeled as class 0
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2Transform images
1000 samples x (3 x 224 x 224)→Resize, crop, convert to tensor, normalize pixel values→1000 samples x (3 x 224 x 224) tensor
Image tensor with pixel values normalized between -1 and 1
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3Train/test split
1000 samples→Split dataset into 800 training and 200 testing samples→Training: 800 samples, Testing: 200 samples
Training set has 160 images of class 0, Testing set has 40 images of class 0
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4Model training
800 samples x (3 x 224 x 224)→Train a CNN classifier on training data→Trained model weights
Model learns to classify images into 5 classes
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5Prediction
1 image tensor (3 x 224 x 224)→Model outputs class probabilities→1 x 5 vector of probabilities
[0.1, 0.7, 0.05, 0.1, 0.05] means class 1 predicted with 70% confidence
Training Trace - Epoch by Epoch
Loss 1.5 |**** 1.1 |*** 0.9 |** 0.7 |* 0.6 |
| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | 1.5 | 0.40 | Model starts learning, accuracy is low |
| 2 | 1.1 | 0.55 | Loss decreases, accuracy improves |
| 3 | 0.9 | 0.65 | Model is learning useful features |
| 4 | 0.7 | 0.75 | Good progress, accuracy rising |
| 5 | 0.6 | 0.80 | Model converging well |
Prediction Trace - 5 Layers
Layer 1: Input image tensor
Layer 2: Convolutional layers
Layer 3: Pooling layers
Layer 4: Fully connected layer
Layer 5: Softmax activation
Model Quiz - 3 Questions
Test your understanding
What does the folder structure tell the model during loading?
Key Insight
Organizing images in folders by class allows easy labeling. Transformations prepare images for the model. Training reduces loss and improves accuracy. Softmax outputs probabilities for class prediction.