Update README.md

README.md

@@ -4,9 +4,12 @@ license: cc-by-nc-4.0
 
 Checkpoints of models in CanadaWildFireDaily benchmark.
 
-Standard UNet(architecture: 'unet'): The baseline spatial U-Net model.
-Age-Encoding UNet(architecture: 'unet_age'): A U-Net that explicitly encodes the satellite age (the time gap in days between the fire event and the satellite acquisition).
-Spatiotemporal UNet(architecture: 'unet_convlstm'): A U-Net featuring aConvLSTMbottleneck for recurrent time-series processing (e.g., 3-day sliding window forecasting).
-Attention UNet(architecture: 'unet_attention'): A U-Net utilizing attention gates in the skip connections to help the model focus on the most critical spatial features and suppress irrelevant background noise.
-UNet-SegFormer(architecture: 'unet_segformer'): A hybrid vision-transformer architecture that replaces the standard CNN encoder with SegFormer's Mix Vision Transformer (MiT), paired with a standard U-Net decoder for heavy pixel-level accuracy.
-UT-AE(architecture: 'utae'): A temporal attention encoder-decoder baseline adapted from the ICCV 2021 U-TAE model for satellite image time series. This baseline uses the time-series offline samples fromTimeseries_Samples/, and the generator now stores sequence positions for the temporal attention encoder when you regenerate those samples.
+## Mono-temporal models:
+
+* Standard UNet(architecture: 'unet'): The baseline spatial U-Net model.
+* Age-Encoding UNet(architecture: 'unet_age'): A U-Net that explicitly encodes the satellite age (the time gap in days between the fire event and the satellite acquisition).
+* Attention UNet(architecture: 'unet_attention'): A U-Net utilizing attention gates in the skip connections to help the model focus on the most critical spatial features and suppress irrelevant background noise.
+* UNet-SegFormer(architecture: 'unet_segformer'): A hybrid vision-transformer architecture that replaces the standard CNN encoder with SegFormer's Mix Vision Transformer (MiT), paired with a standard U-Net decoder for heavy pixel-level accuracy.
+## Multi-temporal models:
+* Spatiotemporal UNet(architecture: 'unet_convlstm'): A U-Net featuring aConvLSTMbottleneck for recurrent time-series processing (e.g., 3-day sliding window forecasting).
+* UT-AE(architecture: 'utae'): A temporal attention encoder-decoder baseline adapted from the ICCV 2021 U-TAE model for satellite image time series. This baseline uses the time-series offline samples fromTimeseries_Samples/, and the generator now stores sequence positions for the temporal attention encoder when you regenerate those samples.