Update README.md

README.md

@@ -235,19 +235,16 @@ accuracy across different resolutions and representations:
 
 1. **Primary Predictions Loss**: 
    - This term computes the L1 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.
+     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 L1 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.
+     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 L1 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.
-
-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.
+     predictions and the references. This encourages the model to produce predictions that maintain accuracy even under slight modifications in the data representation.
 
 ## Computing Infrastructure