notebooks/image_classification/pytorch_image_classification/README.md

Transfer Learning for Image Classification using PyTorch

This notebook uses image classification models from Torchvision that were originally trained using ImageNet and does transfer learning with the Food101 dataset, a flowers dataset, or a custom image dataset.

The notebook performs the following steps:

1. Import dependencies and setup parameters
2. Prepare the dataset
3. Predict using the original model
4. Transfer learning
5. Visualize the model output
6. Export the saved model

Running the notebook

To run the notebook, follow the instructions to setup the PyTorch notebook environment.

References

Dataset citations:

@inproceedings{bossard14,
  title = {Food-101 -- Mining Discriminative Components with Random Forests},
  author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
  booktitle = {European Conference on Computer Vision},
  year = {2014}
}

@ONLINE {tfflowers,
author = "The TensorFlow Team",
title = "Flowers",
month = "jan",
year = "2019",
url = "http://download.tensorflow.org/example_images/flower_photos.tgz" }