diff --git a/scripts/examples/25-Machine-Learning/tf_mobilenet_search_whole_window.py b/scripts/examples/25-Machine-Learning/tf_mobilenet_search_whole_window.py index 8a2c559d6..92ce31381 100644 --- a/scripts/examples/25-Machine-Learning/tf_mobilenet_search_whole_window.py +++ b/scripts/examples/25-Machine-Learning/tf_mobilenet_search_whole_window.py @@ -9,6 +9,8 @@ # learning to apply the model to a target problem by re-training the model. # # NOTE: This example only works on the OpenMV Cam H7 Pro (that has SDRAM) and better! +# To get the models please see the CNN Network library in OpenMV IDE under +# Tools -> Machine Vision. The labels are there too. # # In this example we slide the detector window over the image and get a list # of activations. Note that use a CNN with a sliding window is extremely compute diff --git a/scripts/examples/25-Machine-Learning/tf_mobilenet_serach_just_center.py b/scripts/examples/25-Machine-Learning/tf_mobilenet_serach_just_center.py index 1c243533c..1371a877f 100644 --- a/scripts/examples/25-Machine-Learning/tf_mobilenet_serach_just_center.py +++ b/scripts/examples/25-Machine-Learning/tf_mobilenet_serach_just_center.py @@ -9,6 +9,8 @@ # learning to apply the model to a target problem by re-training the model. # # NOTE: This example only works on the OpenMV Cam H7 Pro (that has SDRAM) and better! +# To get the models please see the CNN Network library in OpenMV IDE under +# Tools -> Machine Vision. The labels are there too. # # In this example we slide the detector window over the image and get a list # of activations. Note that use a CNN with a sliding window is extremely compute