718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
66 lines
2.0 KiB
Markdown
66 lines
2.0 KiB
Markdown
# Darknet To NCNN Conversion Tools
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This is a standalone darknet2ncnn converter without additional dependency.
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Support yolov4, yolov4-tiny, yolov3, yolov3-tiny and enet-coco.cfg (EfficientNetB0-Yolov3).
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Another conversion tool based on darknet can be found at: [darknet2ncnn](https://github.com/xiangweizeng/darknet2ncnn)
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## Usage
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```
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Usage: darknet2ncnn [darknetcfg] [darknetweights] [ncnnparam] [ncnnbin] [merge_output]
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[darknetcfg] .cfg file of input darknet model.
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[darknetweights] .weights file of input darknet model.
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[cnnparam] .param file of output ncnn model.
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[ncnnbin] .bin file of output ncnn model.
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[merge_output] merge all output yolo layers into one, enabled by default.
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```
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## Example
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### 1. Convert yolov4-tiny cfg and weights
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Download pre-trained [yolov4-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg) and [yolov4-tiny.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) or with your own trained weight.
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Convert cfg and weights:
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```
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./darknet2ncnn yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.param yolov4-tiny.bin 1
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```
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If succeeded, the output would be:
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```
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Loading cfg...
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WARNING: The ignore_thresh=0.700000 of yolo0 is too high. An alternative value 0.25 is written instead.
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WARNING: The ignore_thresh=0.700000 of yolo1 is too high. An alternative value 0.25 is written instead.
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Loading weights...
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Converting model...
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83 layers, 91 blobs generated.
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NOTE: The input of darknet uses: mean_vals=0 and norm_vals=1/255.f.
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NOTE: Remember to use ncnnoptimize for better performance.
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```
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### 2. Optimize graphic
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```
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./ncnnoptimize yolov4-tiny.param yolov4-tiny.bin yolov4-tiny-opt.param yolov4-tiny-opt.bin 0
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```
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### 3. Test
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build examples/yolov4.cpp and test with:
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```
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./yolov4 dog.jpg
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```
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The result will be:
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## How to run with benchncnn
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Set 2=0.3 for Yolov3DetectionOutput layer.
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