1.更新conda
conda update conda2.安装tensorfow
conda install tensorflow3.安装opencv
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda install --channel https://conda.anaconda.org/menpo opencv
4.安装darkflow,假设你已经下载好源码，切换到源码目录
pip install -e .5.执行以下命令验证安装是否成功
flow --h如果看到如下信息则表示安装成功
Example usage: flow --imgdir sample_img/ --model cfg/yolo.cfg --load bin/yolo.weights
Arguments:
--help, --h, -h show this super helpful message and exit
--imgdir path to testing directory with images
--binary path to .weights directory
--config path to .cfg directory
--dataset path to dataset directory
--labels path to labels file
--backup path to backup folder
--summary path to TensorBoard summaries directory
--annotation path to annotation directory
--threshold detection threshold
--model configuration of choice
--trainer training algorithm
--momentum applicable for rmsprop and momentum optimizers
--verbalise say out loud while building graph
--train train the whole net
--load how to initialize the net? Either from .weights or a checkpoint, or even from scratch
--savepb save net and weight to a .pb file
--gpu how much gpu (from 0.0 to 1.0)
--gpuName GPU device name
--lr learning rate
--keep Number of most recent training results to save
--batch batch size
--epoch number of epoch
--save save checkpoint every ? training examples
--demo demo on webcam
--queue process demo in batch
--json Outputs bounding box information in json format.
--saveVideo Records video from input video or camera
--pbLoad path to .pb protobuf file (metaLoad must also be specified)
--metaLoad path to .meta file generated during --savepb that corresponds to .pb file
6.执行转换
flow --model cfg/yolo.cfg --load /your path/yolov2.weights --savepb当你看到如下信息，表示转换成功，可以在/your path/darkflow/built_graph 下找到转换好的文件。
Successfully identified 203934260 bytes
Finished in 0.02666330337524414s
Model has a coco model name, loading coco labels.
Building net ...
Source | Train? | Layer description | Output size
-------+--------+----------------------------------+---------------
| | input | (?, 608, 608, 3)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 608, 608, 32)
Load | Yep! | maxp 2x2p0_2 | (?, 304, 304, 32)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 304, 304, 64)
Load | Yep! | maxp 2x2p0_2 | (?, 152, 152, 64)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 152, 152, 128)
Load | Yep! | conv 1x1p0_1 +bnorm leaky | (?, 152, 152, 64)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 152, 152, 128)
Load | Yep! | maxp 2x2p0_2 | (?, 76, 76, 128)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 76, 76, 256)
Load | Yep! | conv 1x1p0_1 +bnorm leaky | (?, 76, 76, 128)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 76, 76, 256)
Load | Yep! | maxp 2x2p0_2 | (?, 38, 38, 256)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 38, 38, 512)
Load | Yep! | conv 1x1p0_1 +bnorm leaky | (?, 38, 38, 256)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 38, 38, 512)
Load | Yep! | conv 1x1p0_1 +bnorm leaky | (?, 38, 38, 256)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 38, 38, 512)
Load | Yep! | maxp 2x2p0_2 | (?, 19, 19, 512)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 19, 19, 1024)
Load | Yep! | conv 1x1p0_1 +bnorm leaky | (?, 19, 19, 512)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 19, 19, 1024)
Load | Yep! | conv 1x1p0_1 +bnorm leaky | (?, 19, 19, 512)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 19, 19, 1024)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 19, 19, 1024)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 19, 19, 1024)
Load | Yep! | concat [16] | (?, 38, 38, 512)
Load | Yep! | conv 1x1p0_1 +bnorm leaky | (?, 38, 38, 64)
Load | Yep! | local flatten 2x2 | (?, 19, 19, 256)
Load | Yep! | concat [27, 24] | (?, 19, 19, 1280)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 19, 19, 1024)
Load | Yep! | conv 1x1p0_1 linear | (?, 19, 19, 425)
-------+--------+----------------------------------+---------------
Running entirely on CPU
2019-03-10 22:43:51.794420: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-03-10 22:43:51.797996: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance.
Finished in 22.694877862930298s
Rebuild a constant version ...
Done