Update README.md to reflect newest eval result

README.md

@@ -44,26 +44,26 @@ The experiment by running the mmseg stack for 80 epochs using the above config l
 
 |     **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% |
+| Natural Vegetation | 0.4038 | 46.89% |
+|       Forest       | 0.4747 | 66.38% |
+|        Corn        | 0.5491 | 65.47% |
+|      Soybeans      | 0.5297 | 67.46% |
+|      Wetlands      | 0.402  | 58.91% |
+|  Developed/Barren  | 0.3611 | 56.49% |
+|     Open Water     | 0.6804 | 90.37% |
+|    Winter Wheat    | 0.4967 | 67.16% |
+|       Alfalfa      | 0.3084 | 66.75% |
+|Fallow/Idle Cropland| 0.3493 | 59.23% |
+|       Cotton       | 0.3237 | 66.94% |
+|       Sorghum      | 0.3283 | 73.56% |
+|        Other       | 0.3427 | 47.12% |
 
 |**aAcc**|**mIoU**|**mAcc**|
 |:------:|:------:|:------:|
-| 54.32% | 0.3692 | 59.51% |
+| 60.64% | 0.4269 | 64.06% |
 
 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
-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.
+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. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification-demo)**.