This is a binary image classification project using Convolutional Neural Networks and TensorFlow API (no Keras) on Python 3. Read all story in Turkish.
It is a ready-to-run code.
pip3 install -r requirements.txt
jupyter lab Binary_classification.ipynb or jupyter notebook Binary_classification.ipynb
No MNIST or CIFAR-10.
This is a repository containing datasets of 5000 training images and 1243 testing images.No problematic image.
train_data_bi.npy is containing 5000 training photos with labels.
test_data_bi.npy is containing 1243 testing photos with labels.
Classes are table & glass. Classes are equal.
Download pure data from here. Warning 1.4 GB.
Training on GPU:
python3 binary_image_classification_GPU.py
Training on CPU:
python3 binary_image_classification_CPU.py
AlexNet is used as architecture. 5 convolution layers and 3 Fully Connected Layers with 0.5 Dropout Ratio. 60 million Parameters.
Trained 5 epochs. Accuracy, AUC and Loss graphs are below:
