Part 1. Train Your YOLO Model

1.1 Install environment dependencies

Refer to ( for details.

1.2 Build training tools

git clone darknet
mkdir build_releasecd build_release
cmake ..
cmake --build . --target install --parallel 8

1.3 Prepare training datasets.

Place the training set pictures in the train folder and the verification set in the Val folder.

1.4 Mark the datasets

git clone
cmake .

Please refer to in the Yolo_mark directory for details.

1.5 Configure parameter files

In addition to the two datasets, several parameter files need to be configured before starting the training.


It states the total number of paths and categories of all the above files. If you use your own dataset, please modify the corresponding parameters before labeling.


It contains all target class names.

  • train.txt

It contains all training image paths; val.txt file is not required. You can manually split 30% of the images from the training file for certification.

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  • yolo.cfg: topology
  • Yolo.conv: Pre-training weight

###span class="_2PHJq public-DraftStyleDefault-ltr"yolov4-tiny.conv.29)or (yolov3-tiny.cfg+yolov3-tiny.conv.11). The cfg file can be found directly in the darknet/cfg directory.

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Confirgure Cfg file. Search the location of all YOLO layers in the cfg file. If there are three types of targets in total, define the [Yolo] layer classes parameter as 3, and then define the filters of the [convolutional] layer on the [Yolo] layer as 24. The calculation formula is filters = (classes + 5) * 3.

yolov4-tiny.cfg has two Yolo layers, so a total of 4 parameters need to be modified.

1.6 Train

If step 1.2 is compiled successfully, the ./darknet tool will be generated in the darknet directory.

Type in the below command:

./darknet detector train ./ ./yolov4-tiny.cfg ./yolov4-tiny.conv.29  -map 

If the GPU is lower than 1080Ti, there may be an error due to insufficient memory. At this time, it is necessary to change the batch parameter to no more than 8 (8, 4, 2, 1) in the first layer [net] of cfg.

If the training goes well, you can see the training log chart as shown in the figure below.

After training, you can see a series of .weights files. Here, it is still recommended to set a validation set when making a dataset, so that the highest weight of the map in the validation set yolov4-tiny_best.weights can be directly locked for subsequent use.

Part 2. Transfer Model Format

IR is the reference format for the OpenVINO tool kit. In this part, we will transfer the ‘Darknet’ model to ‘blob’ with Intermediate ‘TensorFlow’ and ‘IR’.

2.1 Darknet to TensorFlow

Here we need to use obj.names and yolov4-tiny_best.weights created in Step 1.5.

git clone
cd tensorflow-yolov4-tiny
python --class_names obj.names --weights_file yolov4-tiny_best.weights --tiny

2.2 Tensorflow to IR(IR is the OpenVINO inference format)

  • Modify json configuration file

Open yolo_v4_tiny.json in the tensorflow-yolov4-tiny directory, change the classes value to your own number of categories. This file is needed for OpenVINO to do TensorFlow transformation.

  • Replace json configuration file
cp ./yolo_v4_tiny.json /opt/intel/openvino/deployment_tools/model_optimizer/extensions/front/tf
  • Enter OpenVINO model transformation tool directory
cd /opt/intel/openvino/deployment_tools/model_optimizer
  • Transfer command
python --input_model yolov4-tiny.pb --transformations_config ./extensions/front/tf/yolo_v4_tiny.json --batch...
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