add inference example

README.md

@@ -34,7 +34,11 @@ The model follows the [original mae repo](https://github.com/facebookresearch/ma
 4. adding infrared bands besides RGB
 
 ### Inference and demo
-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 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).
+There is an inference script (`Prithvi_run_inference.py`) that allows to run the image reconstruction on a set of three HLS images (see example below). These images have to be 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_100.yaml --checkpoint /path/to/checkpoint/Prithvi_100.pth --output_dir /path/to/out/dir/ --mask_ratio 0.5
+```
 
 ### Finetuning examples
 Two examples of finetuning the model for image segmentation (i.e. flood detection and burn scars detection) using the mmsegmentation library are available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/tree/main/fine-tuning-examples).