Using transfer learning in PyTorch to train a model and classify flower images into 102 different classes. Achieved 85.6% accuracy after 10 epochs.
- PyTorch Framework with torchvision
- CUDA 9.0
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
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