update README

examples/hugging_face/object_detection/README.md

@@ -34,6 +34,12 @@ Let's take a look at some of the things we can do with Kangas.
 
 Here we're going to filter for example images that contain accessories that were not detected by the model.
 
+To do this, we'll run the following query in the filter section.
+
+```
+{"accessories"} != 0 and {"score_accessories"} == 0
+```
+
 https://user-images.githubusercontent.com/7529846/201136371-0321ba11-1be5-484b-800e-014733013d60.mov
 
 ### Sort by highest and lowest mAP score to find our best and worst predictions
@@ -49,15 +55,16 @@ It is likley that our model is detecting some objects well and missing others. W
 
 https://user-images.githubusercontent.com/7529846/201143588-061f8a68-2233-407e-a0cc-72a52fe58c44.mov
 
+Here we see that the average mAP score is much better in images that contain shoes.
+
 ### Filtering based on Image metadata
 
 We have logged annotations and labels as metadata in our Images. Kangas lets you filter examples based on this metadata.
 
-Let's take a look at images that contain bags and shoes. Simply add the following line to the filter input
+Let's take a look at images that contain bags and shoes. Simply add the following line to the filter section.
 
 ```
 {"Image"}.labels.contains("gt_bags") and {"Image"}.labels.contains("gt_shoes")
 ```
 
 https://user-images.githubusercontent.com/7529846/201147784-009906ac-e4fc-4436-a398-ac51cb0677c2.mov
-