Developing an Image Classifier with Deep Learning

Image Classifier Project: Udacity - Machine Learning - Introduction Nanodegree Program

Project goal

• The first part of the project consists of implementing an image classifier with PyTorch using a Jupyter notebook.
• The second part consists of building a command line application that others can use

Data

The project is using this dataset of 102 flower categories. It can be downloaded from here.

Software and Libraries

This project uses the following software and Python libraries:

• Python
• NumPy
• pandas
• Matplotlib
• PyTorch

You also need to have additional software installed to run and execute a Jupyter Notebook.

If you do not have Python installed, I highly recommend installing the Anaconda distribution of Python, which already has the above packages and more included.

Run

In a terminal or command window, navigate to the top-level project directory finding_donors/ (that contains this README) and run one of the following commands:

ipython notebook Image Classifier Project.ipynb

or

jupyter notebook Image Classifier Project.ipynb

For the command line app

• Train a new network on a data set with train.py
  • Basic usage: python train.py data_directory
  • Prints out training loss, validation loss, and validation accuracy as the network trains
  • Options:
    • Set directory to save checkpoints: python train.py data_dir --save_dir save_directory
    • Choose architecture: python train.py data_dir --arch "vgg13"
    • Set hyperparameters: python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 20
    • Use GPU for training: python train.py data_dir --gpu
• Predict flower name from an image with predict.py along with the probability of that name. That is, you'll pass in a single image /path/to/image and return the flower name and class probability.
  • Basic usage: python predict.py /path/to/image checkpoint
  • Options:
    • Return top KK most likely classes: python predict.py input checkpoint --top_k 3
    • Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_to_name.json
    • Use GPU for inference: python predict.py input checkpoint --gpu

This will open the iPython Notebook software and project file in your browser.

Note

Both the Jupyter Notebook file and the HTML version of it are pretty big and may not load correctly inside GitHub. You have to clone the project locally