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.txtcommand.