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1 OpenVINO转换caffe模型步骤 
进入目录 
C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer 
python mo_caffe.py -h 
 
 
参数如下: 
- input_proto:prototxt文件所在位置
 
 - k:CustomLayersMapping.xml文件所在位置
 
 - input_mode:
 
 - mean_file:
 
 - mean_file_offsets:
 
 - output_dir
 
 - scale SCALE
 
 - log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}]
 
 - input
 
 - mean_values
 
 - data_type {FP16,FP32,half,float}] [–disable_fusing]
 
 - disable_resnet_optimization
 
 - finegrain_fusing
 
 - enable_concat_optimization
 
 - extensions
 
 - silent
 
 - freeze_placeholder_with_value
 
 - generate_deprecated_IR_V2
 
 - mean_file_offsets
 
 - disable_omitting_optional
 
 - enable_flattening_nested_params
 
 
 
  
 
2 OpenVINO支持的caffe模型 
2-1 Classification models: 
 
- AlexNet
 - VGG-16, VGG-19
 - SqueezeNet v1.0, SqueezeNet v1.1
 - ResNet-50, ResNet-101, Res-Net-152
 - Inception v1, Inception v2, Inception v3, Inception v4
 - CaffeNet
 - MobileNet
 - Squeeze-and-Excitation Networks: SE-BN-Inception, SE-Resnet-101, SE-ResNet-152, SE-ResNet-50, SE-ResNeXt-101, SE-ResNeXt-50
 - ShuffleNet v2
 
 
  
2-2 Object detection models: 
- SSD300-VGG16, SSD500-VGG16
 - Faster-RCNN
 - RefineDet (Myriad plugin only)
 
 
  
2-3 Face detection models: 
2-4 Semantic segmentation models: 
 
3 OpenVINO支持的caffe层与其在Intermediate Representation (IR)中的对应关系|  NUMBER | LAYER NAME IN CAFFE* | LAYER NAME IN THE INTERMEDIATE REPRESENTATION |  |  1 |  Input |  Input |  |  2 |  GlobalInput |  Input |  |  3 |  InnerProduct |  FullyConnected |  |  4 |  Dropout |  Ignored, does not appear in IR |  |  5 |  Convolution |  Convolution |  |  6 |  Deconvolution |  Deconvolution |  |  7 |  Pooling |  Pooling |  |  8 |  BatchNorm |  BatchNormalization |  |  9 |  LRN |  Norm |  |  10 |  Power |  Power |  |  11 |  ReLU |  ReLU |  |  12 |  Scale |  ScaleShift |  |  13 |  Concat |  Concat |  |  14 |  Eltwise |  Eltwise |  |  15 |  Flatten |  Flatten |  |  16 |  Reshape |  Reshape |  |  17 |  Slice |  Slice |  |  18 |  Softmax |  Softmax |  |  19 |  Permute |  Permute |  |  20 |  ROIPooling |  ROIPooling |  |  21 |  Tile |  Tile |  |  22 |  ShuffleChannel |  Reshape + Split + Permute + Concat |  |  23 |  Axpy |  ScaleShift + Eltwise |  |  24 |  BN |  ScaleShift |  |  25 |  DetectionOutput |  DetectionOutput |  |  26 |  StridedSlice |  StridedSlice |  |  27 |  Bias |  Eltwise(operation = sum) |  
  
 
 
版权声明:本文为CSDN博主「mingo_敏」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。 
原文链接:https://blog.csdn.net/shanglianlm/article/details/89291009 
 
 
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