Dog and cat image classifier with deep learning.
Dog: 0.92035621 Cat: 0.04618423 |
Cat: 0.90135497 Dog: 0.09642436 |
Layer: 1 Kernel: 4 |
Layer: 2 Kernel: 16 |
Layer: 3 Kernel: 10 |
Data/Layer_Outputs
folder for other outputs.python3 predict.py <ImageFileName>
python3 train.py
tensorboard --logdir=Data/Checkpoints/./logs
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
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.
sudo pip3 install -r requirements.txt
command.