Multi-class-Flower-Classifier

Using transfer learning in PyTorch to train a model and classify flower images into 102 different classes. Achieved 71% accuracy after 3 epochs.

Environment

• PyTorch Framework with torchvision
• Python 3.7
• CUDA 9.0

Training

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

Classification

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