Update config name

README.md

@@ -36,7 +36,7 @@ The model follows the [original MAE repo](https://github.com/facebookresearch/ma
 There is an inference script (`Prithvi_run_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 Prithvi_run_inference.py --data_files t1.tif t2.tif t3.tif --yaml_file_path /path/to/yaml/Prithvi_EO_V1_100M_config.yaml --checkpoint /path/to/checkpoint/Prithvi_EO_V1_100M.pth --output_dir /path/to/out/dir/ --input_indices <space separated 0-based indices of channels to select from input> --mask_ratio 0.5 --img_size <length of one side of square input shape>
+python Prithvi_run_inference.py --data_files t1.tif t2.tif t3.tif --yaml_file_path /path/to/yaml/config.yaml --checkpoint /path/to/checkpoint/Prithvi_EO_V1_100M.pth --output_dir /path/to/out/dir/ --input_indices <space separated 0-based indices of channels to select from input> --mask_ratio 0.5 --img_size <length of one side of square input shape>
 ```
 
 This demo is a starting point that can be used as a starting point to generalize to different input shapes / types.