CenterNet MobileNetV2 - inference is too slow

Hi,

I am able to run SSD MobileNetV2 and CenterNet MobileNetV2 (boxes prediction) on my android device. When I compare inference speed of the models on my android device I get below results:

inference of CenterNet MobileNetV2 512x512 is aprox. 5-6 times slower than SSD MobileNetV2 320x320 quantized model.
inference of CenterNet MobileNetV2 512x512 is aprox. 3 times slower than SSD MobileNetV2 320x320 non-quantized model.
inference of CenterNet MobileNetV2 512x512 has aprox. same speed as SSD MobileNetV2 640x640 quantized model.

I run the models on CPU only, using 4 threads based on the official android tflite tutorial (6 month old, hopefully it is not the reason). I have used CenterNet MobileNetV2 512x512 tflite model directly from here: http://download.tensorflow.org/models/object_detection/tf2/20210210/centernet_mobilenetv2fpn_512x512_coco17_kpts.tar.gz (which has btw input size 320x320 instead of 512x512) and I used also CenterNet MobileNetV2 512x512 tflite model created based on this tutorial: https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/centernet_on_device.ipynb
both CenterNet MobileNetV2 512x512 tflite models have the same inference speed.

My understanding is the inference of CenterNet MobileNetV2 512x512 should be aprox. 3 times faster than SSD MobileNetV2 320x320 (based on official documentation/benchmark below)

What is your experience with inference speed of CenterNet MobileNetV2 512x512? Is it fast for you? Am I missing something? Why is CenterNet MobileNetV2 512x512 so slow for me?

thank you