Hello, here are instructions for running inference on our project: 

File Content: 
README.txt - This file
requirements.txt - The python requirments necessary to run this project
train_sample.py - A sample training file which trains a simple model on the data, and save the checkpoint to be loaded
                  in the test_submission.py file.
conform_validation_set.py - python script needed to reformat the validation set to match what the training script is expecting. 
test_submission.py - The file which will return an output for every input in the eval.csv
eval.csv - An example test file
data/get_data.sh - A script which will download the tiny-imagenet data into the data/tiny-imagenet-200 file

Note: You should be using Python 3 to run this code.


To run inferences first install dependencies through `pip3 install -r requirements.txt`, 
and then run script: `python3 test_submission.py eval.csv`.

Training is relatively straightforward as well. 

Please do the following from the directory this file is in:
`pip3 install -r requirements.txt` if you haven't already.
Then run the data downloading script `./data/get_data.sh`
Then conform the validation dataset: `python3 conform_validation_set.py`. A folder called val-fixed will be created
Finally, run `python3 train_sample_torch.py`


Validation of the data is easy too:
`python3 evaluation_tools.py`