Deep Cat Meme Detector: Technical Architecture

Our Deep Cat Meme Detector is built using the latest in feline-specific Deep Learning techniques, featuring:

Convolutional Neural Network (CNN)

• Layers: 10
• Filters: 1000 (with an additional 500 for whisker detection)
• Activation Function: Sigmoid (for those pesky cat whiskers)

Transfer Learning

We've pre-trained on 10,000 images of cats in various states of relaxation, playfulness, and hunger.

This allows us to achieve 99.9% accuracy in detecting:

• Grumpy Cat Mode: 99.2%
• Catnip Overload: 99.5%
• Whisker Distracted: 99.8%

View our training data for more information.

Future Developments

We're currently exploring the integration of:

• Feline Facial Recognition: For those sneaky cats trying to hide their feelings
• Whisker Tracking: To detect even the slightest whisker twitch

Stay tuned for updates and more information on these exciting developments!