diff --git a/README.md b/README.md
index 34413f2..f5e3650 100644
--- a/README.md
+++ b/README.md
@@ -58,6 +58,7 @@ Here you'll train, quantize, compile (on a gpu if possible) and infer (on the ha
         explains well on how to create that. 
    - There's a minimal example dataset in this repository under `/dataset` 
    - To mount this, use eg.: `docker run -it --gpus all -ipc=host -v /path/to/dataset/:/dataset yolov5:v0`
+   - `python train.py --img 640 --batch 16 --epochs 3 --data /dataset/dataset/dataset.yaml --weights yolov5m.pt --cfg models/yolov5m.yaml`
    - For training, make sure you target the correct `--model` and use the correct `--weights` (which are now conveniently already in the hailo docker)
    - once you've saved the best.pb onnx file, you can exit this docker container
 - once you are done with the steps 'training and exporting to ONNX', move on to the next step.