Image-Classifier

Project created as partof the Udacity Nanodegree program "Introduction to Python for AI Programmers"

How to use this app:

Set up enviroment

1. Install Anaconda
2. Verify that Conda has been properly installed by typing the command:
   conda -V
   In response, you should get a similar output:
   conda 23.1.0
3. Go to repo directory: cd \myownpath\Image-Classifier
4. Create a new environment from a yaml file by entering the command:
   conda env create -f torchvision_env.yml
   The environment file contains pytorch library and CUDA drivers.
5. Activate the newly created environment by typing:
   conda activate torchvision_env

Training the neural network

In order to train an image classifier use the train.py script on a particular image dataset. The script takes the image dataset folder as an input and returns a trained neural network as a checkpoint file, which also contains information about the training state and model performance.
The script currently supports three different convolutional neural network models: AlexNet, DenseNet and VGG-16.

Features:

• The script automatically detects the number of classes and rebuilds the network classfier.
• Various options for training are available.

Positional arguments:

• Train image dataset directory:
  dir
  File structure has to be formated as follows:
• image_folder\train\
  • \category_1\
    • \image_1.jpeg
    • \image_2.jpeg
    • ...
    • \image_n.jpeg
  • \category_2\
    • ...
  • \category_n\
    • ...
• image_folder\test\
  • \category_1\
    • \image_1.jpeg
    • \image_2.jpeg
    • ...
    • \image_n.jpeg
  • \category_2\
    • ...
  • \category_n\
    • ...
• image_folder\valid\
  • \category_1\
    • \image_1.jpeg
    • \image_2.jpeg
    • ...
    • \image_n.jpeg
  • \category_2\
    • ...
  • \category_n\
    • ...

Use example:
python train.py flower_data

Optional arguments:

• Display script help:
  -h, --help
  Example usage:
  python train.py -h

• Select convolutional neural network model architecture: AlexNet, DenseNet or VGG-16
  --arch {alexnet, densenet, vgg16}
  Use example:
  python train.py flower_data --arch densenet

• Select learning rate (optimizers step size on each iteration): $\alpha$ < 1
  Default value: $\alpha$ = 0.001
  -lr, --learning_rate
  Use example:
  python train.py flower_data -lr 0.005

• Set the number of hidden units in a classifier (default: 512)
  -hu, --hidden_units
  Use example:
  python train.py flower_data --hidden_units 256

• Choose the number of epochs to train (default: 20)
  -e, --epochs
  Example usage:
  python train.py flower_data -e 50

• Set directory to save checkpoint file (default: cwd\checkpoint.pth)
  -s, --save_dir
  The file name must end with the extension .pt or .pth
  Use example:
  python train.py flower_data --save_dir C:\Users\Tomasz\Desktop\Image-Classifier\alexnet.pt

• Accelerate learning by moving computation to the GPU
  --gpu
  Example usage:
  python train.py flower_data --gpu