Update README.md

README.md

@@ -1,10 +1,10 @@
-> This is a template for code README.md accompanying a Machine Learning paper
+> 👉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. 
+> 👉Optional: include a graphic explaining your approach or main result. 
 
 ## Requirements
 
@@ -14,9 +14,7 @@ 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
+> 👉Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc...
 
 ## Training
 
@@ -26,7 +24,7 @@ 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.
+> 👉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
 
@@ -36,7 +34,7 @@ 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. 
+> 👉Describe how to evaluate the trained models on benchmarks reported in the paper, give example commands. 
 
 ## Pre-trained models
 
@@ -44,19 +42,17 @@ 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). 
+> 👉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:
+Our model achieves the following performance on :
 
-| Model name      | Top 1 Accuracy on [ImageNet](https://paperswithcode.com/sota/image-classification-on-imagenet) |
-| --------------- |-------------------- |
-| my main model   |     85%             |
-| my small model  |     79%             |
+- [Image Classification on ImageNet](https://paperswithcode.com/sota/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. 
-
-> Alternatively, if your main result is a figure, include that figure and link to the command or notebook to reproduce it. 
+> 👉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.