Update README.md

README.md

@@ -190,14 +190,6 @@ its performance on the most recent data points, ensuring its relevance and relia
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
 
-
-
-### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-More information needed
-
 ## Results 
 
 In our evaluation, the proposed model displayed a significant enhancement over the established baseline, which employs bicubic interpolation for the same task. 
@@ -233,25 +225,53 @@ This network is trained to learn the residuals of the bicubic interpolation.
 
 The specific parameters of this network are available in [config.json](https://huggingface.co/predictia/convswin2sr_mediterranean/blob/main/config.json).
 
+## 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:
+
+1. **Primary Predictions Loss**: 
+   - This term computes the \( \mathcal{L}_1 \) loss between the primary model predictions and the reference values. It ensures that the transformer's outputs
+   - closely match the ground truth across the primary spatial resolution.
+
+2. **Downsampled Predictions Loss**: 
+   - Recognizing the importance of accuracy across varying resolutions, this term calculates the \( \mathcal{L}_1 \) loss between the downsampled versions of the
+   - predictions and the reference values. By incorporating this term, the model is incentivized to preserve critical information even when the data is represented
+   - at a coarser scale.
+
+3. **Blurred Predictions Loss**: 
+   - To ensure the model's robustness against small perturbations and noise, this term evaluates the \( \mathcal{L}_1 \) loss between blurred versions of the
+   - predictions and the references. This encourages the model to produce predictions that maintain accuracy even under slight modifications in the data representation.
 
-## Compute Infrastructure
+By combining these loss terms, the Swin2 transformer is trained to produce accurate predictions across different resolutions and under various data transformations, 
+ensuring its versatility and robustness in diverse scenarios.
 
-The use of GPUs in deep learning projects significantly accelerates model training and inference, leading to substantial reductions in computation time and making it feasible to tackle complex tasks and large datasets with efficiency.
+## Technical Infrastructure
 
-The generosity and collaboration of our partners are instrumental to the success of this projects, significantly contributing to our research and development endeavors.
+Leveraging GPUs in deep learning initiatives greatly amplifies the pace of model training and inference. This computational edge not only diminishes the total 
+computational duration but also equips us to proficiently navigate complex tasks and extensive datasets.
 
-- **AI4EOSC**: AI4EOSC stands for "Artificial Intelligence for the European Open Science Cloud." The European Open Science Cloud (EOSC) is a European Union initiative that aims to create a federated environment of research data and services. AI4EOSC is a specific project or initiative within the EOSC framework that focuses on the integration and application of artificial intelligence (AI) technologies in the context of open science.
+Our profound gratitude extends to our collaborative partners, whose invaluable contribution and support have been cornerstones in the fruition of this project. 
+Their substantial inputs have immensely propelled our research and developmental strides.
 
-- **European Weather Cloud**: The European Weather Cloud is the cloud-based collaboration platform for meteorological application development and operations in Europe. Services provided range from delivery of weather forecast data and products to the provision of computing and storage resources, support and expert advice.
+- **AI4EOSC**: Representing "Artificial Intelligence for the European Open Science Cloud," AI4EOSC functions under the aegis of the European Open Science Cloud (EOSC).
+  Initiated by the European Union, EOSC endeavors to orchestrate a cohesive platform for research data and services. AI4EOSC, a distinct arm within EOSC, concentrates
+  on embedding and leveraging artificial intelligence (AI) techniques within the open science domain.
 
-### Hardware
+- **European Weather Cloud**: Serving as a cloud-centric hub, this platform catalyzes collective efforts in meteorological application design and operations
+  throughout Europe. Its offerings are manifold, ranging from disseminating weather forecast data to proffering computational prowess, expert counsel, and
+  consistent support.
 
-For our project, we have deployed two virtual machines (VMs), each featuring a dedicated Graphics Processing Unit (GPU). One VM is equipped with a 16GB GPU, while the other boasts a more substantial 20GB GPU. This resource configuration allows us to efficiently manage a wide range of computing tasks, from data processing to model training and sampling, and ultimately drives the successful execution of our project.
+### Hardware Specifications
 
+Our endeavor harnesses the capabilities of two virtual machines (VMs), each embedded with a dedicated GPU. One VM is bolstered with a 16GB GPU, while its counterpart 
+is equipped with an even potent 20GB GPU. This strategic hardware alignment proficiently caters to diverse computational needs, spanning data orchestration to model 
+fine-tuning and evaluation, ensuring the seamless flow and success of our project.
 
-### Software
+### Software Resources
 
-The code used to train and evaluate this model is freely available through its GitHub Repository [ECMWFCode4Earth/DeepR](https://github.com/ECMWFCode4Earth/DeepR) hosted in the ECWMF Code 4 Earth organization.
+For enthusiasts and researchers inclined towards a deeper probe, our model's training and evaluation code is transparently accessible. 
+Navigate to our GitHub Repository [ECMWFCode4Earth/DeepR](https://github.com/ECMWFCode4Earth/DeepR) under the ECWMF Code 4 Earth consortium.
 
 
 ### Authors