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.
pip3 install -r requirements.txt
Training on GPU:
python3 multiclass_classification_gpu.py
Training on CPU:
python3 multiclass_classification_cpu.py
jupyter lab Multiclass_classification.ipynb or jupyter notebook Multiclass_classification.ipynb
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.
AlexNet is used as architecture. 5 convolution layers and 3 Fully Connected Layers with 0.5 Dropout Ratio. 60 million Parameters. 
Accuracy score reached 87% on CV after just 5 epochs. 
Predictions for first 64 testing images are below. Titles are the predictions of our Model.
