Model quantification

In the ‘Model Training’ section, we explained how to train a yolov8 model, but in order to deploy it on Grove Vision V2, we need to further quantify the model. This section is described below:

• Model INT8 quantification

• Model optimisation

Model INT8 quantification

First of all, we need to get the pt model file. In ‘Model Training’, I explained that we can get the trained pt model file through local training and cloud training.

local

cloud

Here's the translation:

Create a new environment using Anaconda (for example, you can name it petoi_convert_local), and in the new environment, execute the following commands in sequence:

pip install ultralytics
pip install tensorflow

(! Note: We have used anaconda to create a new petoi_train_local environment in the model training section, while the petoi_convert_local environment and petoi_train_local environment used in this section are two different environments, and petoi_train_local environment must not be used for the following operations.)

Next, we need to quantify the model and execute it:

yolo export model=${your model path} format=tflite imgsz=192 int8

You will see a yolov8n_saved_model folder in the current folder containing the yolov8n_full_integer_quant.tflite model file.

Model optimisation

Next, we will perform model optimization. If you are using a Windows computer, you need to install Microsoft C++ Build Tools. If you are a Mac or Linux user, you do not need to install it."

Microsoft C++ 生成工具 - Visual StudioVisual Studio

Execute：

pip3 install ethos-u-vela

This tool is used to compile a TensorFlow Lite for Microcontrollers neural network model into an optimised version that can run on an embedded system containing an Arm Ethos-U NPU.

Create vela_config.ini, copy the following to vela_config.ini

; file: my_vela_cfg.ini ; ----------------------------------------------------------------------------- 
; Vela configuration file ; ----------------------------------------------------------------------------- 
; System Configuration 

; My_Sys_Cfg 
[System_Config.My_Sys_Cfg] 
core_clock=400e6 
axi0_port=Sram 
axi1_port=OffChipFlash 
Sram_clock_scale=1.0 
Sram_burst_length=32 
Sram_read_latency=16 
Sram_write_latency=16 
Dram_clock_scale=0.75 
Dram_burst_length=128 
Dram_read_latency=500 
Dram_write_latency=250 
OnChipFlash_clock_scale=0.25 
OffChipFlash_clock_scale=0.015625 
OffChipFlash_burst_length=32 
OffChipFlash_read_latency=64 
OffChipFlash_write_latency=64 
; ----------------------------------------------------------------------------- 
; Memory Mode 
; My_Mem_Mode_Parent 
[Memory_Mode.My_Mem_Mode_Parent] 
const_mem_area=Axi1 
arena_mem_area=Axi0 
cache_mem_area=Axi0

Execute in the terminal：

vela --accelerator-config ethos-u55-64 --config vela_config.ini --system-config My_Sys_Cfg --memory-mode My_Mem_Mode_Parent --output-dir ${Save path of the optimized model} ${The path of the tflite model that needs to be optimized}

Replace ${Save path of the optimised model} with the directory you want to output to.

Replace ${The path of the tflite model that needs to be optimised} with the quantized model file you just got.

You will then get the model ending in ‘_vela’, which is the model file that can be used for deployment on Grove Vision V2.