Update README.md

README.md

@@ -40,17 +40,20 @@ model = AnySat(size="base", pretrained=True) #Exists also "small" and "tiny"
 ```
 
 To get features from an observation of a batch of observations, you need to provide to the model a dictionnary where keys are from the list: 
-  - "aerial": Single date tensor (Bx4xHxW) with 4 channels (RGB NiR), 0.2m resolution
-  - "aerial-flair": Single date tensor (Bx5xHxW) with 5 channels (RGB NiR Elevation), 0.2m resolution
-  - "spot": Single date tensor (Bx3xHxW) with 3 channels (RGB), 1m resolution
-  - "naip": Single date tensor (Bx4xHxW) with 3 channels (RGB), 1.25m resolution
-  - "s2": Time series tensor (BxTx10xHxW) with 10 channels (B2 B3 B4 B5 B6 B7 B8 B8a B11 B12), 10m resolution
-  - "s1-asc": Time series tensor (BxTx2xHxW) with 2 channels (VV VH), 10m resolution
-  - "s1": Time series tensor (BxTx3xHxW) with 3 channels (VV VH Ratio), 10m resolution
-  - "alos": Time series tensor (BxTx3xHxW) with 3 channels (HH HV Ratio), 30m resolution
-  - "l7": Time series tensor (BxTx6xHxW) with 6 channels (B1 B2 B3 B4 B5 B7), 30m resolution
-  - "l8": Time series tensor (BxTx11xHxW) with 11 channels (B8 B1 B2 B3 B4 B5 B6 B7 B9 B10 B11), rescaled to 10m resolution
-  - "modis": Time series tensor (BxTx7xHxW) with 7 channels (B1 B2 B3 B4 B5 B6 B7), 250m resolution
+| Dataset       | Description                       | Tensor Size                                          | Channels                                  | Resolution |
+|---------------|-----------------------------------|-----------------------------------------|-------------------------------------------|------------|
+| aerial        | Single date tensor |Bx4xHxW                                              | RGB, NiR                                  | 0.2m       |
+| aerial-flair  | Single date tensor |Bx5xHxW                                              | RGB, NiR, Elevation                       | 0.2m       |
+| spot          | Single date tensor |Bx3xHxW                                              | RGB                                       | 1m         |
+| naip          | Single date tensor |Bx4xHxW                                               | RGB                                       | 1.25m      |
+| s2            | Time series tensor |BxTx10xHxW                                          | B2, B3, B4, B5, B6, B7, B8, B8a, B11, B12 | 10m        |
+| s1-asc        | Time series tensor |BxTx2xHxW                                             | VV, VH                                     | 10m        |
+| s1            | Time series tensor |BxTx3xHxW                                            | VV, VH, Ratio                             | 10m        |
+| alos          | Time series tensor |BxTx3xHxW                                            | HH, HV, Ratio                             | 30m        |
+| l7            | Time series tensor |BxTx6xHxW                                            | B1, B2, B3, B4, B5, B7                    | 30m        |
+| l8            | Time series tensor |BxTx11xHxW                                           | B8, B1, B2, B3, B4, B5, B6, B7, B9, B10, B11 | 10m        |
+| modis         | Time series tensor |BxTx7xHxW                                            | B1, B2, B3, B4, B5, B6, B7                | 250m       |
+
 
 Time series keys require a "{key}_dates" (for example "s2_dates") tensor of size BxT that value an integer that represent the day of the year.
 Then you have to choose at which scale you want te produce features. Scale argument is in meters and represent the size of the desired patch size.