Technical Details: Cat Meme Detector

Our feline friends are the true masters of the internet. That's why we've developed a state-of-the-art Cat Meme Detector using Neural Network Topology.

Network Architecture:

Our cat meme detector uses a Convolutional Neural Network (CNN) architecture with the following layers:

1. Conv2D layer with 256 filters, 3x3 kernel, and ReLU activation
2. Max Pooling layer with 2x2 pool size and 2 stride
3. Flatten layer
4. Dense layer with 128 units, ReLU activation, and Dropout 0.2
5. Output layer with 1000 units, Softmax activation, and no dropout

Training Data:

Our model was trained on a dataset of 10,000 cat memes, carefully curated by our team of expert memeologists. The dataset includes various cat breeds, poses, and expressions.

Some examples of our training data include:

• Grumpy cat in a tutu
• Cats playing the trombone
• Cats riding unicycles
• Cats wearing tiny hats

Performance Metrics:

Our model achieves 99.9% accuracy on a test set of 1000 new, unseen memes.

We're proud to say that our model can detect 99.9% of all cat memes, and 99.99% of all memes that aren't cat memes (but let's be real, what's the point of those?)

Want to know more about our cat meme detector? Visit: