Update README.md

README.md

@@ -42,6 +42,10 @@ python Prithvi_run_inference.py --data_files t1.tif t2.tif t3.tif --yaml_file_pa
 ### Finetuning examples
 Examples of finetuning the model for image segmentation using the mmsegmentation library are available through Hugging Face (e.g. [burn scars segmentation](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-burn-scar), [flood mapping](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-sen1floods11), and [multi temporal crop classification](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification)), with the code used for the experiments available on [github](https://github.com/NASA-IMPACT/hls-foundation-os/tree/main/fine-tuning-examples). This also contains instructions to finetune the model for flood detection on the popular open access [sen1floods11 dataset](https://github.com/cloudtostreet/Sen1Floods11).
 
+### Feedback
+
+Your feedback is invaluable to us. If you have any feedback about the model, please feel free to share it with us. You can do this by submitting issues on our open-source repository, [hls-foundation-os](https://github.com/NASA-IMPACT/hls-foundation-os/issues), on GitHub.
+
 ### Citation
 
 If this model helped your research, please cite `Prithvi-100M` in your publications. Here is an example BibTeX entry: