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Cats vs Dogs Classification
CNN to classify cat and dog images. Data augmentation, training, and model optimization on a Kaggle dataset.
Accuracy: 78%
Stack
What happened?
- 01
The question
Can a Kaggle-trained CNN really learn the difference between a cat and a dog?
- 02
The problem
Images vary: background, size, position. A naive network memorizes instead of generalizing.
- 03
The hypothesis
With data augmentation and a classic CNN architecture, the network will learn generalizable patterns.
- 04
The build
CNN trained on the Kaggle Cats vs Dogs dataset: data augmentation, training, hyperparameter optimization.
- 05
The failure
78% accuracy is good… but the errors were not random: short-haired cats and dark dogs were confused with each other.
- 06
The discovery
Errors tell you what the model hasn't seen. Data augmentation helped, but some classes are inherently close.
- 07
The result
A 78%-accuracy classifier, trained and optimized on the Kaggle dataset.
- 08
The next frontier
Transfer learning on a pretrained model (ResNet), calibration, and fine-grained error analysis.