Update README.md

README.md

@@ -40,7 +40,8 @@ Code for Finetuning is available through [github](https://github.com/NASA-IMPACT
 Configuration used for finetuning is available through this [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/fine-tuning-examples/configs/sen1floods11.py).
 
 ### Results
-The experiment by running the mmseg stack for 80 epochs using the above config led to the following result:
+
+Finetuning the geospatial foundation model for 100 epochs leads to the following performance on out-of-sample test data:
 
 |     **Classes**    | **IoU**| **Acc**|
 |:------------------:|:------:|:------:|
@@ -52,5 +53,20 @@ The experiment by running the mmseg stack for 80 epochs using the above config l
 | 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:
+
+
+|     **Classes**    | **IoU**| **Acc**|
+|:------------------:|:------:|:------:|
+|      No water      | 95.37% | 97.39% |
+|     Water/Flood    | 77.95% | 88.74% |
+
+|**aAcc**|**mIoU**|**mAcc**|
+|:------:|:------:|:------:|
+| 96.02% | 86.66% | 93.07% |
+
+Finetuning took ~1 hour on a 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)**.