My awesome paper title

This repository is the official implementation of My awesome 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

You can download pretrained models here:

Mymodel 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).

Results

Our model achieves the following performance on :

Image Classification on ImageNet

Model name	Top 1 Accuracy	Top 5 Accuracy
Mymodel	85%	95%

📋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.