TensorFlow-Multiclass-Image-Classification-using-CNN-s

This is a multiclass image classification project using Convolutional Neural Networks and TensorFlow API (no Keras) on Python.

It is a ready-to-run code.

Read all story in Turkish.

Dependencies

pip3 install -r requirements.txt

Training

Training on GPU:

python3 multiclass_classification_gpu.py

Training on CPU:

python3 multiclass_classification_cpu.py

Notebook

jupyter lab Multiclass_classification.ipynb or jupyter notebook Multiclass_classification.ipynb

Data

No MNIST or CIFAR-10.

This is a repository containing datasets of 5200 training images of 4 classes and 1267 testing images.No problematic image.

Just extract files from multiclass_datasets.rar.

train_data_bi.npy is containing 5200 training photos with labels.

test_data_bi.npy is containing 1267 testing photos with labels.

Classes are chair & kitchen & knife & saucepan. Classes are equal(1300 glass - 1300 kitchen - 1300 knife- 1300 saucepan) on training data.

Download pure data from here. Warning 962 MB.

Architecture

AlexNet is used as architecture. 5 convolution layers and 3 Fully Connected Layers with 0.5 Dropout Ratio. 60 million Parameters.

Results

Accuracy score reached 87% on CV after just 5 epochs.

Predictions

Predictions for first 64 testing images are below. Titles are the predictions of our Model.