/GoogLeNet-from-Scratch

Implementing the MiniGoogLeNet architecture using the Miniception

Primary LanguagePython

MiniGoogLeNet-Miniception

Implementing the MiniGoogLeNet architecture using the Miniception

Requirements:

  • python3
  • keras
  • numpy V1.19.2
  • scikit-image V0.17.2
  • opencv-python
  • matplotlib V3.2.2
  • tensorflow

CNN architectures implemented in this repository:

MiniGoogLeNet architectue using the Miniception.

  • Miniception module is a smaller variant of the Inception module.

  • Inside the Inception module, we learn all three 5x5, 3x3, 1x1 filters (computing them in parallel) concatenating the resulting feature maps along the channel dimension. This process enables GoogLeNet to learn both local features via smaller convolutions and abstracted features with larger convolutions.

CIFAR-10 Dataset:

Dataset consisting of 60,000 (32x32x3 RGB images) resulting in a feature vector dimensionality of 3072. It consists of 10 classes: airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks.

Evaluations of the Trained Networks:

MiniGoogLeNet Classification: 79% Accuracy on average minigooglenet

References:

  • Deep Learning for Computer Vision with Python VOL1 & VOL2 by Dr.Adrian Rosebrock