This repository is the official implementation of My Paper Title.

📋 Optional: include a graphic explaining your approach/main result, bibtex entry, link to demos, blog posts and tutorials

📋 Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc... To install requirements:

pip install -r requirements.txt

To train the model(s) in the paper, run this command:

python train.py --config <path_to_config_file>

📋 Describe how to train the models, with example commands on how to train the models in your paper, including the full training procedure and appropriate hyperparameters.

To evaluate my model on ImageNet, run:

python eval.py --model-file mymodel.pth --benchmark imagenet

📋 Describe how to evaluate the trained models on benchmarks reported in the paper, give commands that produce the results (section below).

This project is setup as a package which means you can now easily import any file into any other file like so:

from project.datasets.mnist import mnist
from project.lit_classifier_main import LitClassifier
from pytorch_lightning import Trainer

# model
model = LitClassifier()

# data
train, val, test = mnist()

# train

trainer = Trainer()
trainer.fit(model, train, val)

# test using the best model!
trainer.test(test_dataloaders=test)

You can download pretrained models here:

• My awesome model trained on ImageNet using parameters x,y,z.

📋 Give a link to where/how the pretrained models can be downloaded and how they were trained (if applicable). Alternatively you can have an additional column in your results table with a link to the models.

Our model achieves the following performance on :

📋 Include a table of results from your paper, and link back to the leaderboard for clarity and context. If your main result is a figure, include that figure and link to the command or notebook to reproduce it.

📋 Pick a licence and describe how to contribute to your code repository.

@article{YourName,
  title={Your Title},
  author={Your team},
  journal={Location},
  year={Year}
}