Update README.md

README.md

@@ -35,7 +35,7 @@ Labels are from CDL(Crop Data Layer) and classified into 13 classes.
 The Prithvi-100m model was initially pretrained using a sequence length of 3 timesteps. For this task, we leverage the capacity for multi-temporal data input, which has been integrated from the foundational pretrained model. This adaptation allows us to achieve more generalized finetuning outcomes.
 
 ### 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/)
 
 Configuration used for finetuning is available through [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/fine-tuning-examples/configs/multi_temporal_crop_classification.py).
 
@@ -66,4 +66,4 @@ It is important to acknowledge that the CDL (Crop Data Layer) labels employed in
 
 
 ### Inference
-There is an inference script that allows to run the hls-cdl crop classification model for inference on HLS images. These input have to be geotiff format, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order.
+The github repo includes an inference script an inference script that allows to run the hls-cdl crop classification model for inference on HLS images. These input have to be geotiff format, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order.