海思3159A运行yolov3（三）——darknet2caffe

最新推荐文章于 2024-06-01 21:49:21 发布

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最新推荐文章于 2024-06-01 21:49:21 发布 · 802 阅读

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文章标签：

#caffe #yolov3

本文介绍如何将YOLOv3和YOLOv4模型转换为Caffe框架下可用的格式，包括环境配置、参数设置、模型转换及验证等步骤。通过调整Caffe源代码并重新编译，使Caffe支持YOLOv3和YOLOv4特有的层。

可以参考原作者：https://github.com/ChenYingpeng/darknet2caffe

一、环境

Python2.7

Caffe

Pytorch >= 0.40

二、caffe参数配置

1. caffe_layers/mish_layer/mish_layer.hpp,caffe_layers/upsample_layer/upsample_layer.hpp into include/caffe/layers/.
2. Copy caffe_layers/mish_layer/mish_layer.cpp mish_layer.cu,caffe_layers/upsample_layer/upsample_layer.cpp upsample_layer.cu into src/caffe/layers/.
3. Copy caffe_layers/pooling_layer/pooling_layer.cpp into src/caffe/layers/.Note:only work for yolov3-tiny,use with caution.
4. Add below code into src/caffe/proto/caffe.proto.

// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
message LayerParameter {
  optional TileParameter tile_param = 138;
  optional VideoDataParameter video_data_param = 207;
  optional WindowDataParameter window_data_param = 129;
++optional UpsamplePara