Update README.md

README.md

@@ -4,11 +4,19 @@ library_name: keras
 
 ## Model description
 
-More information needed
+This model identifies contrails in satellite images. It takes pre-processed .npy files (images) as its inputs, and returns a "mask" image showing only the contrails overlayed on the same area. 
+We used a TransUNet model architecture ...
 
 ## Intended uses & limitations
 
-More information needed
+Note, this is in progress - 
+
+We hope that data scientists and researchers focused on reducing contrails (towards the goal of reducing global warming) will use this model to improve their work. 
+There are current efforts underway to develop software that re-routes planes to avoid contrails. Researchers are building models to predict contrails based on atmospheric conditions and other factors, but they need a way to validate those predictions.
+That's where we come in. Let's say you have a model that suggests there should be contrails in an image (based on the time/location the picture was taken).
+Our model can find the contrails (or lack thereof) in your image without a human labeler, allowing you to validate whether your predictions were correct. 
+This becomes valuable at scale, when you need to validate your model on many images - contrail detection is a tough task for humans and machines alike!
+
 
 ## How to Get Started with the Model
 
@@ -26,10 +34,13 @@ model = from_pretrained_keras("MIDSCapstoneTeam/ContrailSentinel", custom_object
 
 ## Training and evaluation data
 
-More information needed
+(will add more info here) 
+OpenContrails dataset [here](https://arxiv.org/abs/2304.02122)
 
 ## Training procedure
 
+
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: