Source Code of Project Image Classification with PyTorch 🔥
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assets
flowers
Image Classifier Project.ipynb
README.md
cat_to_name.json
predict.py
setup.py
train.py
workspace_utils.py

README.md

Image Classification with PyTorch

Project code for Udacity's Data Scientist Nanodegree program. In this project, students first develop code for an image classifier built with PyTorch, then convert it into a command line application.

Prerequisites

The Code is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip.

To install pip run in the command Line

python -m ensurepip -- default-pip 

to upgrade it

python -m pip install -- upgrade pip setuptools wheel

to upgrade Python

pip install python -- upgrade

Additional Packages that are required are: Numpy, Pandas, MatplotLib, Pytorch, PIL and json.
You can donwload them using pip

pip install numpy pandas matplotlib pil

or conda

conda install numpy pandas matplotlib pil

In order to install PyTorch head over to the PyTorch site select your specs and follow the instructions given.

Project assets:

  • Image Classifier Project.ipynb Jupyter Notebook
  • Image Classifier Project.html HTML export of the Jupyter Notebook above.
  • setup.py Main Script
  • train.py Command line tool to train a new network on a data set.
  • predict.py Command line tool to to predict flower name from an image.

Example Image for Training

Image categories are found in cat_to_name.json and flower images you can get in this repository itself or can be downloaded in the gziped tar file flower_data.tar.gz from Udacity.

Get flower images:

mkdir -p flowers && cd flowers
curl https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz | tar xz

You should now have test, train and valid directories containing classification directories and flower images under the flowers directory.

Viewing the Jupyter Notebook

In order to better view and work on the Jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to see it without any problem. Alternatively you can clone the repository using

git clone https://github.com/rowhitswami/Image-Classification-with-PyTorch/
cd Image-Classification-with-PyTorch/

Open terminal in current folder and type:

jupyter notebook

locate the notebook and run it.

Command Line Application

  • Train a new network on a data set with train.py

    • Basic Usage : python train.py data_directory
    • Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains
    • Options:
      • Set direcotry to save checkpoints: python train.py data_dir --save_dir save_directory
      • Choose arcitecture (densenet161 or vgg16 available): pytnon train.py data_dir --arch "vgg16"
      • Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_units 512 256 --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 with /path/to/checkpoint and return the flower name and class probability

    • Basic usage: python predict.py /path/to/image checkpoint
    • Options:
      • Return top K 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