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-# readme-template
-Best practices README.md template for a ML research code repository
+> This is a template for code README.md accompanying a Machine Learning paper
+
+# My owesome paper title
+
+This repository is the official implementation of [My owesome paper title](https://arxiv.org/abs/2030.12345). 
+
+> 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...
+
+> Best practice: include a requirements.txt in your repository
+
+## 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:
+
+- [mymodel.pth](https://drive.google.com/filehash)
+
+> 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 on (ImageNet)[https://paperswithcode.com/sota/image-classification-on-imagenet] |
+| --------------- |-------------------- |
+| my main model   |     85%             |
+| my small model  |     79%             |
+
+> Include a table of results from your paper, and link back to the leaderboard to give readers more context in the future. 
+
+> Alternatively, if your main result is a figure, include that figure and link to the command or notebook to reproduce it. 
+
+