the inference time of tflite_quant is larger than tflite

[youngboy52]

I use the supported model file (model_1.tflite, model_2.tflite, model_quant_1.tflite and model_quant_2.tflite) and the script "real_time_processing_tf_lite.py" to compare the inference time.
My implementation configs: Ubuntu 18.04, tf2.0.
the processing times are shown as follows:
TF-lite: 0.383403 ms; TF-lite quantized: 0.4470351 ms
It is a little abnormal that TF-lite quantized model is slower than TF-lite model during inference. I found the script is required in tf2.3.0 when running tflite model. Does it mean that tf2.0 has some limitations in your script? Looking forward to your reply

[wwbnjsace]

i use the onnx model to inference,but it has a Very high CPU usage. so i want to know how to hava a low cput usage?