Dog and cat image classifier with deep learning
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README.md

Dog-Cat Classifier

By Arda Mavi

Dog and cat image classifier with deep learning.

Example:

Dog: 0.92035621
Cat: 0.04618423
Cat: 0.90135497
Dog: 0.09642436
Layer outputs of test photographs:
Layer: 1
Kernel: 4
Layer: 2
Kernel: 16
Layer: 3
Kernel: 10
Look up Data/Layer_Outputs folder for other outputs.

Using Predict Command:

python3 predict.py <ImageFileName>

Model Training:

python3 train.py

Using TensorBoard:

tensorboard --logdir=Data/Checkpoints/./logs

Model Architecture:

  • Input Data Shape: 64x64x3

Layer 1:

  • Convolutional Layer 32 filter Filter shape: 3x3

  • Activation Function: ReLu

  • Max Pooling Pool shape: 2x2

Layer 2:

  • Convolutional Layer 32 filter Filter shape: 3x3

  • Activation Function: ReLu

  • Max Pooling Pool shape: 2x2

Layer 3:

  • Convolutional Layer 64 filter Filter shape: 3x3

  • Activation Function: ReLu

  • Max Pooling Pool shape: 2x2

Classification:

  • Flatten

  • Dense Size: 64

  • Activation Function: ReLu

  • Dropout Rate: 0.5

  • Dense Size: 2

  • Activation Function: Sigmoid

Optimizer: Adadelta
Loss: Binary Crossentropy

Adding new train dataset:

If you want to add new dataset to datasets, you create a directory and rename what you want to add category (like 'cat' or 'phone').

If you want to add a new training image to previously category datasets, you add a image to about category directory and if you have npy files in Data folder delete npy_train_data folder.

Note: We work on 64x64 image also if you use bigger or smaller, program will automatically return to 64x64.

Important Notes:

  • Used Python Version: 3.6.0
  • Install necessary modules with sudo pip3 install -r requirements.txt command.