nalbert9 / ai_app_classification_flowers_python Goto Github PK

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.

• 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.

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

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.

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