Update README.md

README.md

@@ -49,9 +49,9 @@ Finetuning the geospatial foundation model for 100 epochs leads to the following
 |      No water      | 96.90% | 98.11% |
 |     Water/Flood    | 80.46% | 90.54% |
 
-|**aAcc**|**mIoU**|**mAcc**|
-|:------:|:------:|:------:|
-| 97.25% | 88.68% | 94.37% |
+|     **aAcc**       |**mIoU**|**mAcc**|
+|:------------------:|:------:|:------:|
+|       97.25%       | 88.68% | 94.37% |
 
 
 The performance of the model has been further validated on an unseen, holdout flood event in Bolivia. The results are consistent with the performance on the test set:
@@ -62,12 +62,12 @@ The performance of the model has been further validated on an unseen, holdout fl
 |      No water      | 95.37% | 97.39% |
 |     Water/Flood    | 77.95% | 88.74% |
 
-|**aAcc**|**mIoU**|**mAcc**|
-|:------:|:------:|:------:|
-| 96.02% | 86.66% | 93.07% |
+|     **aAcc**       |**mIoU**|**mAcc**|
+|:------------------:|:------:|:------:|
+|       96.02%       | 86.66% | 93.07% |
 
-Finetuning took ~1 hour on a NVIDIA V100.
+Finetuning took ~1 hour on an NVIDIA V100.
 
 
 ### Inference
-The github repo includes an inference script that allows to run the flood mapping model for inference on Sentinel-2 images. These input have to be geotiff format, including 6 bands for a single time-step described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-sen1floods11-demo)**.
+The github repo includes an inference script that allows running the flood mapping model for inference on Sentinel-2 images. These inputs have to be geotiff format, including 6 bands for a single time-step described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-sen1floods11-demo)**.