Updated json

README.md

@@ -36,7 +36,7 @@ The model follows the [original MAE repo](https://github.com/facebookresearch/ma
 There is an inference script (`inference.py`) that allows to run the image reconstruction on a set of HLS images assumed to be from the same location at different time steps(see example below). These should be provided in chronological order in geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR 1, 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).
 
 ```
-python inference.py --data_files t1.tif t2.tif t3.tif t4.tif --input_indices <optional, space separated 0-based indices of the six Prithvi channels in your input>
+python inference.py --data_files t1.tif t2.tif t3.tif --input_indices <optional, space separated 0-based indices of the six Prithvi channels in your input>
 ```
 
 This demo is a starting point that can be used as a starting point to generalize to different input shapes / types.