Flowers Classification using PyTorch

In this project, we first develop code for an image classifier built with PyTorch, then convert it into a command line application. GPU is necessary for training the Deep Learning model.

Accomplishment:

• Train Image Classifier Model, which uses Deep Learning Pre-trained Neural Networks (VGG/DenseNet) to train a neural network to recognize different species of flowers (dataset of 102 flower categories);

• Python App which allows user to input some arguments in order to train and make prediction.

Local Environment Instructions

1. Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data

$ git clone https://github.com/nalbert9/AI_App_Classification_Flowers_Python.git

2. Create (and activate) a new Anaconda environment

3. Install PyTorch and torchvision; this should install the latest version of PyTorch

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

Examples

Command Line

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 "vgg16"
  • 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 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

You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at.

License

The contents of this repository are covered under the MIT License.