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README.md
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@@ -31,6 +31,7 @@ The model follows the [original mae repo](https://github.com/facebookresearch/ma
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1. replace 2D patch embed with 3D patch embed;
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2. replace 2D positional embed with 3D positional embed;
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3. replace 2D patchify and unpatchify with 3D.
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### Inference and demo
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There is an inference script (`Prithvi_run_inference.py`) that allows to run the image reconstruction on a set of three HLS images. These images have to be geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in reflectance units. There is also a **demo** that leverages the same code [here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-demo)
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1. replace 2D patch embed with 3D patch embed;
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2. replace 2D positional embed with 3D positional embed;
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3. replace 2D patchify and unpatchify with 3D.
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4. adding infrared bands besides RGB
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### Inference and demo
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There is an inference script (`Prithvi_run_inference.py`) that allows to run the image reconstruction on a set of three HLS images. These images have to be geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in reflectance units. There is also a **demo** that leverages the same code [here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-demo)
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