📋A template README.md for code accompanying a Machine Learning paper
My awesome paper title
This repository is the official implementation of My owesome paper title.
📋Optional: include a graphic explaining your approach or main result.
Requirements
To install requirements:
pip install -r requirements.txt
📋Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc...
Training
To train the model in the paper, run this command:
python train.py --input-data <path_to_data> --alpha 10 --beta 20
📋Describe how to train the model, with example commands on how to train the models in your paper, including the full training procedure and hyperparameter optimisation approach.
Evaluation
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 example commands.
Pre-trained models
We provide links to pretrained models:
📋Give a link to where/how the pretrained models can be downloaded and used (if applicable).
Results
Our model achieves the following performance on :
Image Classification on ImageNet
| Model name | Top 1 Accuracy | Top 5 Accuracy |
|---|---|---|
| My model | 85% | 95% |
📋Include a table of results from your paper, and link back to the leaderboard to give readers more context in the future. If your main result is a figure, include that figure and link to the command or notebook to reproduce it.