openvino object_detection_demo_yolov3_async .pb转.xml/.bin 目标检测

柔光的暖阳◎ 2022-12-11 07:58 108阅读 0赞

### openvino object\_detection\_demo\_yolov3\_async .pb转.xml/.bin 目标检测 ###

### yolov3.xml ###

### yolov3.bin ###

![在这里插入图片描述][20200929160550579.png_pic_center]

#### 查看模型转换支持的模型文件列表models.lst ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTE5NDE3MQ_size_16_color_FFFFFF_t_70_pic_center]

cd C:\Program Files (x86)\IntelSWTools\openvino_2020.4.287\deployment_tools\inference_engine\demos\python_demos\object_detection_demo_yolov3_async

#### models.lst文件内容 ####

# This file can be used with the --list option of the model downloader.
    yolo-v1-tiny-tf
    yolo-v2-tiny-tf
    yolo-v2-tf
    yolo-v3-tf
    mobilefacedet-v1-mxnet

#### yolo-v3-tf ####

yolo-v3-tf为例

#### 查看转换模型参数文件model.yml ####

![\[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-CsgbrhuU-1601366719757)(evernotecid://A7BA1EDC-F70B-4A16-ABAA-EDD379BB023D/appyinxiangcom/29884553/ENResource/p17)\]][img-CsgbrhuU-1601366719757_evernotecid_A7BA1EDC-F70B-4A16-ABAA-EDD379BB023D_appyinxiangcom_29884553_ENResource_p17]

cd C:\Program Files (x86)\IntelSWTools\openvino_2020.4.287\deployment_tools\open_model_zoo\models\public\yolo-v3-tf

#### model.yml内容 ####

description: >-
      YOLO v3 is a real-time object detection model implemented with Keras* from this
      repository <https://github.com/david8862/keras-YOLOv3-model-set> and converted to
      TensorFlow* framework. This model was pretrained on COCO* dataset with 80 classes.
    task_type: detection
    files:
      - name: yolo-v3.pb
        size: 248128731
        sha256: 8ed66d597a936924e98102a5fa16b38569452e17dbb52ddf2877e0f550f57952
        source: https://download.01.org/opencv/public_models/022020/yolo_v3/yolov3.pb
      - name: yolo-v3.json
        size: 384
        sha256: 90f3ea735a2a8908b66dab744b9ec0425fd2564f7b9be6ebd1564dd38d28ec5c
        source: https://download.01.org/opencv/public_models/022020/yolo_v3/yolo_v3_new.json
    model_optimizer_args:
      - --input_shape=[1,416,416,3]
      - --input=input_1
      - --scale_values=input_1[255]
      - --reverse_input_channels
      - --transformations_config=$dl_dir/yolo-v3.json
      - --input_model=$dl_dir/yolo-v3.pb
    framework: tf
    quantizable: yes
    license: https://raw.githubusercontent.com/david8862/keras-YOLOv3-model-set/master/LICENSE

#### 对应路径下载yolov3.pb和yolo\_v3\_new.json ####

source: https://download.01.org/opencv/public_models/022020/yolo_v3/yolov3.pb
        source: https://download.01.org/opencv/public_models/022020/yolo_v3/yolo_v3_new.json

#### 百度网盘下载yolov3.pb和yolo\_v3\_new.json ####

链接：https://pan.baidu.com/s/1w2qJTZAJj9xeHz85387rjQ 
    提取码：46ih 
    复制这段内容后打开百度网盘手机App，操作更方便哦--来自百度网盘超级会员V2的分享

#### yolov3.pb 转yolov3.xml/yolov3.bin ####

cd C:\Program Files (x86)\IntelSWTools\openvino_2020.4.287\deployment_tools\model_optimizer
    
    python mo_tf.py --input_shape=[1,416,416,3] --input=input_1 --scale_values=input_1[255] --reverse_input_channels --transformations_config=C:\Users\AINUC\Desktop\object_detection_demo_yolov3_async\yolo_v3_new.json --input_model=C:\Users\AINUC\Desktop\object_detection_demo_yolov3_async\yolov3.pb

#### 百度网盘下载yolov3.xml/yolov3.bin ####

链接：https://pan.baidu.com/s/1w2qJTZAJj9xeHz85387rjQ 
    提取码：46ih 
    复制这段内容后打开百度网盘手机App，操作更方便哦--来自百度网盘超级会员V2的分享

#### 目标检测执行 ####

python object_detection_demo_yolov3_async.py -m yolov
    3.xml -i 3.mp4 --labels coco.names

#### 效果图 ####

CPU 延迟太高。有GPU的最好

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\[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-0C3SEVo0-1601366719762)(evernotecid://A7BA1EDC-F70B-4A16-ABAA-EDD379BB023D/appyinxiangcom/29884553/ENResource/p21)\]

> > > #### 欢迎大家交流学习，任何问题都可以留言 ####

[20200929160550579.png_pic_center]: /images/20221123/3768191f744d4ecaa0a4fe20c61f2570.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTE5NDE3MQ_size_16_color_FFFFFF_t_70_pic_center]: /images/20221123/ccbc87d3a62d4e8fbe3af8a8ceb7b044.png
[img-CsgbrhuU-1601366719757_evernotecid_A7BA1EDC-F70B-4A16-ABAA-EDD379BB023D_appyinxiangcom_29884553_ENResource_p17]: /images/20221123/78c93e4f87e74c29971b25e70a35d473.png