Update README to reflect correct links; Added finetune results to README

README.md

@@ -17,9 +17,9 @@ metrics:
 - IoU
 ---
 ### Model and Inputs
-The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/README.md) parameter model is finetuned to classify crop and other land cover types based off HLS data and CDL labels from the [HLS Burn Scar Scenes dataset](https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars). 
-This dataset includes input chips of 224x224x18, where 224 is the height and width and 18 is combined with 6 bands of 3 time-steps. 
-The bands are:
+The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/README.md) parameter model is finetuned to classify crop and other land cover types based off HLS data and CDL labels from the [multi_temporal_crop_classification dataset](https://huggingface.co/datasets/ibm-nasa-geospatial/multi-temporal-crop-classification). 
+
+This dataset includes input chips of 224x224x18, where 224 is the height and width and 18 is combined with 6 bands of 3 time-steps. The bands are:
  
 1. Blue
 2. Green
@@ -28,16 +28,42 @@ The bands are:
 5. SWIR 1
 6. SWIR 2
 
-While the Prithvi-100m was pretrained with 3 timesteps, this task utilize the capibility of multi-temporal data input adapted from the pretrained foundation model and provide more generalized and
+Labels are from CDL(Crop Data Layer) and classified into 13 classes.
+
+![](multi_temporal_crop_classification.png)
+
+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)
 
-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/fine-tuning-examples/configs/multi_temporal_crop_classification.py).
 
 ### Results
-The experiment by running the mmseg stack for 80 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 80 epochs using the above config led to the following result:
+
+|     **Classes**    | **IoU**| **Acc**|
+|:------------------:|:------:|:------:|
+| Natural Vegetation | 0.3362 | 39.06% |
+|       Forest       | 0.4362 | 65.88% |
+|        Corn        | 0.4574 | 54.53% |
+|      Soybeans      | 0.4682 | 62.25% |
+|      Wetlands      | 0.3246 | 45.62% |
+|  Developed/Barren  | 0.3077 |  49.1% |
+|     Open Water     | 0.6181 | 90.04% |
+|    Winter Wheat    | 0.4497 | 66.75% |
+|       Alfalfa      | 0.2518 | 63.97% |
+|Fallow/Idle Cropland| 0.328  | 54.46% |
+|       Cotton       | 0.2679 | 66.37% |
+|       Sorghum      | 0.2741 | 75.91% |
+|        Other       | 0.2803 | 39.76% |
+
+|**aAcc**|**mIoU**|**mAcc**|
+|:------:|:------:|:------:|
+| 54.32% | 0.3692 | 59.51% |
+
+It is important to acknowledge that the CDL (Crop Data Layer) labels employed in this process are known to contain noise and are not entirely precise, thereby influencing the model's performance. Fine-tuning the model with more accurate labels is expected to further enhance its overall effectiveness, leading to improved results.
+
 
 ### Inference and demo
 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. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo)**.