利用anaconda新建一个环境（例如可以命令为petoi\_convert\_local），在新环境中依次执行：
```
pip install ultralytics
pip install tensorflow
```
（！注意：我们在模型训练章节的本地训练部分使用anaconda新建了petoi\_train\_local环境，而本节使用的petoi\_convert\_local环境和petoi\_train\_local环境是两个不同的环境，不可使用petoi\_train\_local进行以下操作。）
下面，我们需要对模型进行量化，执行：
```
yolo export model=${your model path} format=tflite imgsz=192 int8
```
你将会在当前文件夹下看到一个 `yolov8n_saved_model` 文件夹，其中包含 `yolov8n_full_integer_quant.tflite` 模型文件。
## 图优化
下面进行图优化。如果您是windows电脑，您需要安装 Microsoft C++ Build Tools。如果您是Mac用户或者Linux用户，则不需要安装。
{% embed url="" %}
执行
```
pip3 install ethos-u-vela
```
创建vela\_config.ini,将以下内容复制到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
```
在终端执行
```
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} ${The path of the tflite model that needs to be optimized}
```
将***${Save path of the optimized model}替换为你想要的输出目录***
将***${The path of the tflite model that needs to be optimized}***替换为你刚才得到的经过量化后的模型文件
然后你就会得到以“\_vela”结尾的模型，这个模型是可以用于部署在Grove Vision V2上的模型文件。
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