Technical Architecture of the Cat Meme Detector

Our team of expert felines and human engineers have crafted a comprehensive system to detect even the most subtle signs of feline memes.

Components

Image Preprocessing

Our system uses a combination of edge detection and thresholding to isolate and enhance feline features.

Image resizing and rotation (up to 90 degrees)
Contrast and brightness adjustment
Filtering (Gaussian, median, and box blur)

Neural Network

We employ a deep learning architecture with a convolutional layer, a fully connected layer, and a softmax activation function.

Input layer with 128x128 pixels and 3 color channels
Convolutional layer with 32x32 filters and a stride of 2
Pool size of 2x2
Fully connected layer with 512 neurons

Meme Classification

We use a custom classification layer to identify the type of meme (e.g., Grumpy Cat, Keyboard Cat, etc.).

Class weights: 0.7 for Grumpy Cat, 0.3 for Keyboard Cat
Threshold for detection: 0.5

Subcomponents

Image Preprocessing Neural Network Meme Classification

Edge Cases

Our system has a few edge cases to account for unusual feline behavior.

Laughing cat: 1.0 confidence, 0.8 certainty
Playing cat: 0.9 confidence, 0.7 certainty
Deadpan cat: 0.4 confidence, 0.2 certainty