Update README.md

README.md

@@ -22,6 +22,9 @@ The data consists of RGB images, sparse spectral samples and instance segmentati
 
 From the 100 images, we extract >430,000 spectral samples, of which >85,000 belong to one of the 19 classes in the dataset. The rest of the spectra can be used for negative sampling when training classifiers.
 
+Additionally, we provide a set of demo-videos in `.lo` format which are unannotated but which can be used to qualititively test algorithms built on this dataset.
+
+
 ### Classes
 
 The dataset contains 19 classes:
@@ -106,6 +109,7 @@ for lo_data_item in training_data:
     )
 
 plot_labelled_spectra(object_spectra_dict, class_numbers_to_labels, ax)
-plt.show()
-    
-```
+plt.show()    
+```
+
+See [here TODO](https://github.com/livingoptics/python-examples) for an example of how to run a spatial-spectral segmentation algoirthm using this dataset.