This is a multiclass image classification & localization project for SINGLE object using CNN's and TensorFlow API (no Keras) on Python3.

1 ) Collecting images via Google Image Download. Only one object must be in the image.

2 ) Labeling images via LabelImg

3 ) Data Augmentation (create_training_data.py). Mirroring with respect to x axis, mirroring with respect to y axis and adding noise were carried out. Hereby, data amount were 8-fold.

4 ) After data augmentation, create_training_data.py script is creating suitable xml files for augmented images(in order not to label all augmented labels).

5 ) Making our data tabular. Input is image that we feed into CNN. Output1 is one hot encoded classification output. Output2 is the locations of bounding boxes(regression) in create_training_data.py.

6 ) Determining hypermaraters in train.py.

7 ) Separating labelled data as train and CV in train.py.

8 ) Defining our architecture in train.py. I used AlexNet for model architecture.

9 ) Creating 2 heads for calculating loss in train.py. One head is classification loss. The other head is regression loss.

10 ) Training the CNN on a GPU (GTX 1050 - One epoch lasted 10 seconds approximately)

11 ) Testing on unseen data (testing_images folder) colled from the Internet(in test.py).

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