Initial creation of README

README.md

@@ -1,3 +1,46 @@
 ---
 license: apache-2.0
+language:
+- en
+tags:
+- Pytorch
+- mmsegmentation
+- segmentation
+- Crop Classification
+- Multi Temporal
+- Geospatial
+- Foundation model
+datasets:
+- ibm-nasa-geospatial/hls_burn_scars
+metrics:
+- accuracy
+- 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:
+ 
+1. Blue
+2. Green
+3. Red
+4. Narrow NIR
+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
+
+### 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
+).
+
+### 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.
+
+### 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)**.
+
+
+