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@@ -19,10 +19,10 @@ We include the trained models:
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  * **Unet multioutput S2-to-L8** in folder `models/WF2_unetv2_bgriswirs`
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  * **Unet multioutput RGBNIR** in folder `models/WF2_unetv2_rgbi`
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-
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  ![metrics_ml4floods](metrics_ml4floods.png)
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- In order to run any of these models in Landsat or Sentinel-2 scene see the tutorial [*Inference with clouds aware floods segmentation model*](https://spaceml-org.github.io/ml4floods/content/ml4ops/HOWTO_Run_Inference_multioutput_binary.html) in the ml4floods docs.
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  If you find this work useful please cite:
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  * **Unet multioutput S2-to-L8** in folder `models/WF2_unetv2_bgriswirs`
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  * **Unet multioutput RGBNIR** in folder `models/WF2_unetv2_rgbi`
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+ The following table shows the performance of the models in the test dataset:
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  ![metrics_ml4floods](metrics_ml4floods.png)
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+ In order to run any of these models in a Landsat or Sentinel-2 scene see the tutorial [*Inference with clouds aware floods segmentation model*](https://spaceml-org.github.io/ml4floods/content/ml4ops/HOWTO_Run_Inference_multioutput_binary.html) in the ml4floods docs.
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  If you find this work useful please cite:
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