Paolo-Fraccaro commited on
Commit
b82b44c
1 Parent(s): 9006c68

correct desc

Browse files
Files changed (1) hide show
  1. app.py +1 -1
app.py CHANGED
@@ -397,7 +397,7 @@ with gr.Blocks() as demo:
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  gr.Markdown(value='# Prithvi image reconstruction demo')
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  gr.Markdown(value='''Prithvi is a first-of-its-kind temporal Vision transformer pretrained by the IBM and NASA team on continental US Harmonised Landsat Sentinel 2 (HLS) data. Particularly, the model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder learning strategy, with a MSE as a loss function. The model includes spatial attention across multiple patchies and also temporal attention for each patch. More info about the model and its weights are available [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M).\n
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  This demo showcases the image reconstracting over three timestamps, with the user providing a set of three HLS images and the model randomly masking out some proportion of the images and then reconstructing them based on the not masked portion of the images.\n
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- The user needs to provide three HLS geotiff images, including the following channels in reflectance units: Blue, Green, Red, NIRa, SWIR, SWIR 2.
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  ''')
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  with gr.Row():
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  with gr.Column():
 
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  gr.Markdown(value='# Prithvi image reconstruction demo')
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  gr.Markdown(value='''Prithvi is a first-of-its-kind temporal Vision transformer pretrained by the IBM and NASA team on continental US Harmonised Landsat Sentinel 2 (HLS) data. Particularly, the model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder learning strategy, with a MSE as a loss function. The model includes spatial attention across multiple patchies and also temporal attention for each patch. More info about the model and its weights are available [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M).\n
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  This demo showcases the image reconstracting over three timestamps, with the user providing a set of three HLS images and the model randomly masking out some proportion of the images and then reconstructing them based on the not masked portion of the images.\n
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+ The user needs to provide three HLS geotiff images, including the following channels in reflectance units: Blue, Green, Red, Narrow NIR, SWIR, SWIR 2.
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  ''')
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  with gr.Row():
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  with gr.Column():