In this Project we implement a paper (https://arxiv.org/pdf/1803.07728.pdf) that uses geometric transformations to extract features of an image without requiring these images to be labeled.

The learning objectives of this project:

1. Learn how to use pytorch Dataset/DataLoader to load and preprocess data
2. Training a model from scratch and frequently checkpointing models
3. Implement good software engineering skills including the use of virtual environments, git (for partner work), and OOP

See results.txt

pip3 install vitrualenv (if not already installed) virtualenv venv source venv/bin/activate pip3 install -r requirements.txt

To deactivate the environment you are in run: source deactivate

main.py will contain the training loop and validation for the model. You can start here to get a general idea of the flow of the code base. resnet.py will contain your implementation of the resnet model (you can find the architecture online or in the paper). data.py will contain all your data loading functions.

Once you have implemented the model, you can start training by running main.py with the following command: python3 main.py --config config.yaml --train --data_dir ./data/cifar-10-batches-py/ --model_number 1

config.yaml contains the configuration file with all the hyperparameters. If you have time, feel free to change these values and see how your model performs.

You can read more about the CIFAR-10 dataset here: https://www.kaggle.com/c/cifar-10

1. Go to this link https://www.cs.toronto.edu/~kriz/cifar.html
2. Right click on "CIFAR-10 python version" and click "Copy Link Address"
3. Go to your CLI and go into the data directory.
4. Run this cURL command to start downloading the dataset: curl -O <URL of the link that you copied>
5. To extract the data from the .tar file run: tar -xzvf <name of file> (type man tar in your CLI to see the different options for running the tar command). NOTE: Each file in the directory contains a batch of images in CIFAR-10 that have been serialized using python's pickle module. You will have to first unpickle the data before loading it into your model.

Please consider the format that the data is in and what format you will need to convert it into in order to train your model. If you are stuck on this, feel free to use Google. Your implementation for getting and loading the data from the model will likely differ from the implementation that I used so feel free to deviate from the skeleton code here.

We use tensorflow to build efficient datapipelines. You can read more about them here. Resource: https://pytorch.org/docs/stable/data.html And an excellennt turotial on how to use these: https://stanford.edu/~shervine/blog/pytorch-how-to-generate-data-parallel

https://www.google.com/search?q=resnet+architecture&tbm=isch&source=iu&ictx=1&fir=nrwHYuY3M7ZNXM%253A%252CmlG8I6OjyTBN4M%252C_&vet=1&usg=AI4_-kRZVFcZ9REeELvn4BDXDpOJhFpNQg&sa=X&ved=2ahUKEwjd5NiphYjkAhVPKa0KHROtD3QQ9QEwBHoECAYQCQ#imgrc=eLRQQc-BgrBkxM:&vet=1

Here is an excellent guide on how to save and restore models in pytorch https://pytorch.org/tutorials/beginner/saving_loading_models.html