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products:sbc:vim3:npu:ksnn:demos:yolov8n

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products:sbc:vim3:npu:ksnn:demos:yolov8n [2023/12/22 04:01]
louis 		products:sbc:vim3:npu:ksnn:demos:yolov8n [2024/02/22 05:19]
louis
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 ``` ```
  
-Refer ''README.md'' to create and train a YOLOv8n model.+Refer ''README.md'' to create and train a YOLOv8n model. My version ''torch==1.10.1'' and ''ultralytics==8.0.86''.
  
 ===== Convert the model ===== ===== Convert the model =====
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 After training the model, modify ''ultralytics/ultralytics/nn/modules/head.py'' as follows. After training the model, modify ''ultralytics/ultralytics/nn/modules/head.py'' as follows.
  
-```diff+```diff head.py
 diff --git a/ultralytics/nn/modules/head.py b/ultralytics/nn/modules/head.py diff --git a/ultralytics/nn/modules/head.py b/ultralytics/nn/modules/head.py
 index 0b02eb3..0a6e43a 100644 index 0b02eb3..0a6e43a 100644
Line 62: Line 62:
 + +
 ``` ```
 +
 +<WRAP important>
 +If you pip-installed ultralytics package, you should modify in package.
 +</WRAP>
  
 Create a python file written as follows to export ONNX model. Create a python file written as follows to export ONNX model.
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 $ python export.py $ python export.py
 ``` ```
 +
 +<WRAP important>
 +Use [[https://netron.app/ | Netron]] to check your model output like this. If not, please check your ''head.py''.
 +
 +{{:products:sbc:vim3:npu:ksnn:yolov8n-vim3-ksnn-output.png?600|}}
 +</WRAP>
  
 Enter ''aml_npu_sdk/acuity-toolkit/python'' and run command as follows. Enter ''aml_npu_sdk/acuity-toolkit/python'' and run command as follows.
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             --quantized-dtype asymmetric_affine \             --quantized-dtype asymmetric_affine \
             --source-files ./data/dataset/dataset0.txt \             --source-files ./data/dataset/dataset0.txt \
 +            --batch-size 1 \
 +            --iterations 1 \
             --kboard VIM3 --print-level 0              --kboard VIM3 --print-level 0 
 ``` ```
  
-If you use ''VIM3L'' , please use ''VIM3L'' to replace ''VIM3''. +If you want to use more quantified images, please modify ''batch-size'' and ''iterations''. ''batch-size''×''iterations''=number of quantified images. 
 + 
 +If you use ''VIM3L'' , please use ''VIM3L'' to replace ''VIM3''.
  
 If run succeed, converted model and library will generate in ''outputs/yolov8n''. If run succeed, converted model and library will generate in ''outputs/yolov8n''.
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 Put ''yolov8n.nb'' and ''libnn_yolov8n.so'' into ''ksnn/examples/yolov8n/models/VIM3'' and ''ksnn/examples/yolov8n/libs'' Put ''yolov8n.nb'' and ''libnn_yolov8n.so'' into ''ksnn/examples/yolov8n/models/VIM3'' and ''ksnn/examples/yolov8n/libs''
 +
 +If your model's classes is not 80, please remember to modify the parameter, ''LISTSIZE''. 
 +
 +```shell
 +LISTSIZE = classes number + 64
 +```
  
 ==== Picture input demo ==== ==== Picture input demo ====
Last modified: 2024/05/27 22:16 by louis

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