How to speed up YoloV5 TFLite inference time in phone app?

I am using the YoloV5 model for custom object recognition, and when I export it to tflite model for inclusion in the mobile app, the resulting time to object recognition is 5201.2ms inference. How can I reduce the inference to optimal for faster recognition? The dataset I use to train is 2200 images and use the model yolov5x to train. Thank for help me !!

You have several options:

1. Train a smaller Yolo model (m instead of x, for example)
2. Resize the images (640x640 to for example 320x320, notice that the dimension need to be a multiple of the maximum stride which is 32)
3. Quantize the model to FP16 or INT8
4. Use NNAPI delegate (only provides speedup if the CPU contains any HW accelerator: GPU, DSP, NN engine)

None of these options exclude each other, all can be used at the same time for maximum inference speed. 1, 2 & 3 will sacrifice model performance for inference speed.