# Model quantification * 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.

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." {% embed url="" %} Execute： ``` pip3 install ethos-u-vela ``` This tool is used to compile a [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers) neural network model into an optimised version that can run on an embedded system containing an [Arm Ethos-U NPU](https://www.arm.com/products/silicon-ip-cpu). 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.

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