Hi all,
I am working with a custom trained (re-trained) ssd mobilenet v2 model from tensorflow model zoo. The re-trained model has an average precision and recall of around 0.5 when inference tested on my development environment. But post conversion to a DLC model and running as an android application, it is returning absurdly low detection scores of below 0.1 as shown below.
Model - ssd_mobilenet_v2_320x320_coco17_tpu-8
Tensorflow Version - 2.5 SNPE version - 1.54.2
Layers:
Input - input_tensor:0,
Output - StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression_classes, StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression_scores, StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression_boxes[/quote]
I am assuming the problem is with model conversion. Any pointers to solving this is much appreciated.
Thanks, Amith
Dear customer,
Thanks for your work on our engine development.
Could you please share the model to us for more deeply analysis?
BTW, you can compare the layer accuracy while execute the SNPE with --debug into snpe-net-run option.
Meanwhile, we're developing the accuracy tool internal and will release outside further.
BR.
Wei