diff --git a/notebooks/fishes/auto-ml-object-detection/README.md b/notebooks/fishes/auto-ml-object-detection/README.md
index 40028c7..e6985cf 100644
--- a/notebooks/fishes/auto-ml-object-detection/README.md
+++ b/notebooks/fishes/auto-ml-object-detection/README.md
@@ -1,11 +1,53 @@
-Owner [Andrew Jansen](mailto:andrew.jansen@awe.gov.au)
+Owner [Andrew Jansen](mailto:andrew.jansen@dcceew.gov.au)
 
 #### Description
+This dataset includes 44,112 images with 82,904 bounding box annotations for 23 tropical freshwater fish taxa from northern Australia.
 
-...
+Images were derived from Remote Underwater Video (RUV) deployments in deep channel and shallow lowland billabongs, Kakadu National Park, Northern Territory Australia. RUV deployments were conducted during the Supervising Scientists annual fish monitoring program in the 2016, 2017 and 2018 recessional flow period (dry season).
+
+•	All images are in .jpg format and are 1920x1080 in dimension.
+
+•	Bounding box annotations are in COCO format.
+
+Fish taxa include:
+
+```
+Ambassis agrammus
+Ambassis macleayi
+Amniataba percoides
+Craterocephalus stercusmuscarum
+Denariusa bandata
+Glossamia aprion
+Glossogobius spp.
+Hephaestus fuliginosus
+Lates calcarifer
+Leiopotherapon unicolor
+Liza ordensis
+Megalops cyprinoides
+Melanotaenia nigrans
+Melanotaenia splendida inornata
+Mogurnda mogurnda
+Nemetalosa erebi
+Neoarius spp.
+Neosilurus spp.
+Oxyeleotris spp.
+Scleropages jardinii
+Strongylura kreffti
+Syncomistes butleri
+Toxotes chatareus
+```
 
 #### Labeling Guide
-...
+To speed up the labelling process and define what makes a "good" annotation for model training we developed the following criteria to standardise labelling across multiple users, projects and geographical regions.
+
+1. Key features, fish were only labelled if key defining characteristics for species level identification were visible in the image, such as colouration or morphology.
+
+2. Orientation, fish directly facing toward or away from the camera were not labelled, as key features are often obscured making species level identification difficult.
+
+3. Depth, as fish move further away from the camera, ability to confidently identify reduces. Orientation combined with deteriorating light and turbidity makes species level identification difficult. Annotations were only made in clear conditions where the above criteria were also met.
+
+4. Obstruction, if a fish was obscured by debris, aquatic vegetation or other fish, bounding boxes are not overlapped or separated into two boxes.
+
 
 #### Azure Deployment