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.
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.
Image Classifier Project.ipynbJupyter NotebookImage Classifier Project.htmlHTML export of the Jupyter Notebook above.setup.pyMain Scripttrain.pyCommand line tool to train a new network on a data set.predict.pyCommand line tool to to predict flower name from an image.
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 xzYou should now have test, train and valid directories containing classification directories and flower images under the flowers directory.
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.
-
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
- Set direcotry to save checkpoints:
- Basic Usage :
-
Predict flower name from an image with
predict.pyalong with the probability of that name. That is you'll pass in a single image/path/to/imagewith/path/to/checkpointand 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
- Return top K most likely classes:
- Basic usage: