Update README.md

README.md

@@ -18,24 +18,24 @@ tags:
 
 # Europe Reanalysis Super Resolution
 
-The aim of the project is to create a Machine learning (ML) model that can generate high-resolution regional reanalysis data (similar to the one produced by CERRA) by downscaling global reanalysis data from ERA5. 
+The aim of the project is to create a Machine learning (ML) model that can generate high-resolution regional reanalysis data (similar to the one produced by CERRA) by 
+downscaling global reanalysis data from ERA5. 
 
+This will be accomplished by using state-of-the-art Deep Learning (DL) techniques like U-Net, conditional GAN, and diffusion models (among others). Additionally, 
+an ingestion module will be implemented to assess the possible benefit of using CERRA pseudo-observations as extra predictors. Once the model is designed and trained, 
+a detailed validation framework takes the place. 
 
-This will be accomplished by using state-of-the-art Deep Learning (DL) techniques like U-Net, conditional GAN, and diffusion models (among others). Additionally, an ingestion module will be implemented to assess the possible benefit of using CERRA pseudo-observations as extra predictors. Once the model is designed and trained, a detailed validation framework takes the place. 
+It combines classical deterministic error metrics with in-depth validations, including time series, maps, spatio-temporal correlations, and computer vision metrics, 
+disaggregated by months, seasons, and geographical regions, to evaluate the effectiveness of the model in reducing errors and representing physical processes. 
+This level of granularity allows for a more comprehensive and accurate assessment, which is critical for ensuring that the model is effective in practice. 
 
+Moreover, tools for interpretability of DL models can be used to understand the inner workings and decision-making processes of these complex structures by analyzing 
+the activations of different neurons and the importance of different features in the input data.
 
-It combines classical deterministic error metrics with in-depth validations, including time series, maps, spatio-temporal correlations, and computer vision metrics, disaggregated by months, seasons, and geographical regions, to evaluate the effectiveness of the model in reducing errors and representing physical processes. This level of granularity allows for a more comprehensive and accurate assessment, which is critical for ensuring that the model is effective in practice. 
+This work is funded by [Code for Earth 2023](https://codeforearth.ecmwf.int/) initiative. The model **ConvSwin2SR** is released in Apache 2.0, making it usable without 
+restrictions anywhere.
 
-
-Moreover, tools for interpretability of DL models can be used to understand the inner workings and decision-making processes of these complex structures by analyzing the activations of different neurons and the importance of different features in the input data.
-
-This work is funded by [Code for Earth 2023](https://codeforearth.ecmwf.int/) initiative. The model **ConvSwin2SR** is released in Apache 2.0, making it usable without restrictions anywhere.
-
-
-
-
-
-#  Table of Contents
+# Table of Contents
 
 - [Model Card for Europe Reanalysis Super Resolution](#model-card-for--model_id-)
 - [Table of Contents](#table-of-contents)
@@ -57,8 +57,10 @@ This work is funded by [Code for Earth 2023](https://codeforearth.ecmwf.int/) in
     - [Metrics](#metrics)
   - [Results](#results)
 - [Technical Specifications](#technical-specifications-optional)
-  - [Model Architecture and Objective](#model-architecture-and-objective)
-  - [Loss function](#loss-function)
+  - [Model Architecture](#model-architecture)
+    - [Components](#components)
+    - [Configuration details](#configuration-details)
+    - [Loss function](#loss-function)
   - [Computing Infrastructure](#computing-infrastructure)
     - [Hardware](#hardware)
     - [Software](#software)
@@ -185,12 +187,6 @@ across different temporal segments.
 The testing data samples correspond to the three-year period from 2018 to 2020, inclusive. This segment is crucial for assessing the model's real-world applicability and 
 its performance on the most recent data points, ensuring its relevance and reliability in current and future scenarios.
 
-
-### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-
 ## Results 
 
 In our evaluation, the proposed model displayed a significant enhancement over the established baseline, which employs bicubic interpolation for the same task. 
@@ -210,23 +206,29 @@ In comparison to the bicubic interpolation baseline, our model's superior predic
   ![rmse](metric_global_map_diff_var-rmse.png)
 
 
-
 # Technical Specifications
 
-## Model Architecture and Objective
+## Model Architecture
+
+Our model's design is deeply rooted in the Swin2 architecture, specifically tailored for Super Resolution (SR) tasks.
+We've harnessed the [transformers library](https://github.com/huggingface/transformers) to streamline and simplify the model's design.
 
-The model architecture is based on the original Swin2 architecture for Super Resolution (SR) tasks. The library [transformers](https://github.com/huggingface/transformers) is used to simplify the model design.
+![Model Architecture](architecture.png)
 
-![architecture](architecture.png)
+### Components
 
-The main component of the model is a [transformers.Swin2SRModel](https://huggingface.co/docs/transformers/model_doc/swin2sr#transformers.Swin2SRModel) which increases x8 the spatial resolution of its inputs (Swin2SR only supports upscaling ratios power of 2).  
-As the real upscale ratio is ~5 and the output shape of the region considered is (160, 240), a Convolutional Neural Network (CNN) is included as a pre-process component which convert the inputs into a (20, 30) feature maps that can be fed to the Swin2SRModel.
+- **Transformers Component**: Central to our model is the [transformers.Swin2SRModel](https://huggingface.co/docs/transformers/model_doc/swin2sr#transformers.Swin2SRModel). This component amplifies the spatial resolution of its inputs by a factor of 8. Notably, Swin2SR exclusively supports upscaling ratios that are powers of 2. 
+- **Convolutional Neural Network (CNN) Component**: Given that our actual upscale ratio is approximately 5 and the designated output shape is (160, 240),
+  we've integrated a CNN. This serves as a preprocessing unit, transforming inputs into (20, 30) feature maps suitable for the Swin2SRModel.
+  
+The underlying objective of this network is to master the residuals stemming from bicubic interpolation.
 
-This network is trained to learn the residuals of the bicubic interpolation.
+### Configuration Details
 
-The specific parameters of this network are available in [config.json](https://huggingface.co/predictia/convswin2sr_mediterranean/blob/main/config.json).
+For those inclined towards the intricacies of the model, the specific parameters governing its behavior are meticulously detailed in the 
+[config.json](https://huggingface.co/predictia/convswin2sr_mediterranean/blob/main/config.json).
 
-## Loss function
+### Loss function
 
 The Swin2 transformer optimizes its parameters using a composite loss function that aggregates multiple \( \mathcal{L}_1 \) loss terms to enhance its predictive 
 accuracy across different resolutions and representations: