image-classifier's Introduction

image-classifier

This is an Image classifier that can train a model from image datasets and can be used to predict images

Specifications

The project submission include at least two files train.py and predict.py. The first file, train.py, will train a new network on a dataset and save the model as a checkpoint. The second file, predict.py, uses a trained network to predict the class for an input image.

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 KK 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

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