Model Pipeline - Cutout and CutMix
This pipeline shows how Cutout and CutMix help improve image classification models by changing training images to make the model learn better. Cutout hides parts of images, and CutMix mixes parts of two images.
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Cutout and CutMix in Computer Vision - Model Pipeline Trace
Data Flow - 4 Stages
1Original Dataset
5000 images x 32 x 32 x 3→Raw images with labels→5000 images x 32 x 32 x 3
Image of a cat with label 'cat'
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2Cutout Augmentation
5000 images x 32 x 32 x 3→Randomly mask a square patch in each image→5000 images x 32 x 32 x 3
Cat image with an 8x8 black square hiding part of the cat
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3CutMix Augmentation
5000 images x 32 x 32 x 3→Mix patches from two images and combine labels proportionally→5000 images x 32 x 32 x 3
Image with half cat and half dog, label is 0.6 cat + 0.4 dog
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4Model Training
Batch of 128 images x 32 x 32 x 3→Train CNN on augmented images and labels→Model weights updated
CNN learns features from images with cutout and cutmix
Training Trace - Epoch by Epoch
Loss
1.2 |*
1.0 | *
0.8 | *
0.6 | *
0.4 | *
0.2 | *
0.0 +--------
1 5 10 15 20 Epochs| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | 1.2 | 0.45 | Model starts learning, loss high, accuracy low |
| 5 | 0.8 | 0.65 | Loss decreases, accuracy improves with augmentation |
| 10 | 0.5 | 0.80 | Model learns robust features, accuracy rises |
| 15 | 0.35 | 0.88 | Loss low, accuracy high, good generalization |
| 20 | 0.30 | 0.90 | Training converged with strong performance |
Prediction Trace - 6 Layers
Layer 1: Input Image
Layer 2: Convolutional Layers
Layer 3: Pooling Layers
Layer 4: Fully Connected Layers
Layer 5: Softmax Activation
Layer 6: Final Prediction
Model Quiz - 3 Questions
Test your understanding
What does the Cutout augmentation do to the training images?
Key Insight
Cutout and CutMix help the model learn better by making training images more varied and challenging. This leads to lower loss and higher accuracy, showing the model generalizes well to new images.