In this project, I have first developed code for an image classifier built with PyTorch (see Jupyter Notebook), then converted it into a command line application .
Image Classifier predicts 102 flower categories. The full dataset can be found here
Due to size restrictions on GitHub, the dataset is not posted here. You will need to download it separately to run the classifier.
Python 3.7.2
- torchvision==0.2.1
- requests==2.19.1
- torch==1.0.1
- Pillow==5.4.1
- numpy==1.16.2
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 0.003 --hidden_units 512 --epochs 5
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
Parts of these code is based on:
- Udacity Introduction to Pytorch Tutorials
- Transfer Learning Tutorial provided by Sasank Chilamkurthy