Update README.md

README.md

@@ -30,16 +30,16 @@ The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi
 It is important to point out that the HLS Burn Scar Scenes dataset includes a single timestep, while the Prithvi-100m was pretrained with 3 timesteps. This highlights the flexibility of this model to adapt to different downstream tasks and requirements.
 
 ### Code
-Code for Finetuning is available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/tree/main/fine-tuning-examples)
+Code for Finetuning is available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/tree/main)
 
-Configuration used for finetuning is available through [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/fine-tuning-examples/configs/firescars_config.py
+Configuration used for finetuning is available through [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/configs/burn_scars.py)
 ).
 
 ### Results
-The experiment by running the mmseg stack for 50 epochs using the above config led to an IoU of **0.72** on the burn scar class and **0.96** overall accuracy. It is noteworthy that this leads to a resonably good model, but further developement will most likely improve performance.
+The experiment by running the mmseg stack for 50 epochs using the above config led to an IoU of **0.73** on the burn scar class and **0.96** overall accuracy. It is noteworthy that this leads to a resonably good model, but further developement will most likely improve performance.
 
 ### Inference and demo
-The github repo includes an inference script that allows to run the burn scar model for inference on HLS images. These input have to be geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in reflectance units [0-1]. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo)**.
+The github repo includes an inference script that allows to run the burn scar model for inference on HLS images. These input have to be geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in reflectance units [0-1]. There is also a [**demo**](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo) that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo)**.