You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hello everyone!
I would like to know what format the input data on the trained model still has. I found some information in this question #52 , but there are a number of clarifications. Is the following true for [batch, channels, timesteps, width, height]:
Are the channels layers from GOES-16/18 ? (then what are these layers for the trained MetNet-2? Wind, humidity, temperature? In what order are they arranged in the tensor ?)
Are the timesteps a grid that sets the prediction interval? (you can train for minutes, hours [1,3 or 6] by setting layers for these intervals [for three hours: {0, 3, 6, 9, 12, 15....}])
Should the images be pre-cropped to the specified dimensions of 512x512 (256x256) and centered around the point of interest?
What is the essence of the batch ? What data do they express in the input parameters? Are these pressure levels?
In general, it would be nice to make a more constructive example of running the model (what format and structure are the inputs and outputs in the trained example for MetNet-2).
Thanks in advance for the answer!
The text was updated successfully, but these errors were encountered:
Hello everyone!
I would like to know what format the input data on the trained model still has. I found some information in this question #52 , but there are a number of clarifications. Is the following true for [batch, channels, timesteps, width, height]:
GOES-16/18
? (then what are these layers for the trainedMetNet-2
? Wind, humidity, temperature? In what order are they arranged in the tensor ?)In general, it would be nice to make a more constructive example of running the model (what format and structure are the inputs and outputs in the trained example for
MetNet-2
).Thanks in advance for the answer!
The text was updated successfully, but these errors were encountered: