yolov3/yolov4 reference here ,weclome to star~~~ https://github.com/cuixing158/yolov3-yolov4-matlab

@cui :
Thank you very much for sharing your work! I think that new modules and functionalities such as exporter you created would be really helpful for MATLAB users.

As for your question related to conv bias, if Conv layer is followed by BN layer, this importer set the parameter of conv bias to BN layer as an offset, and set 0 to bias of Conv layer. This is to get the same behavior as Darknet.

layer_conv.Bias = zeros(1,1,filters,'single');
...
layer_bn = batchNormalizationLayer('Name', lname);
layer_bn.Offset = conv_bias;
...

Dear,Kei Otsuka,
I refactored your code, rearranged the network architecture based on Module, supported groupConvolution, fullyconnected, etc. In addition, I added the exportDarknetworks function. You can refer to here https://github.com/cuixing158/DeepLearning-Converter-for-Darknet-Matlab-Model-Format

Dear,Kei Otsuka,
From your "createConv2d" function, why does the conv bias take 0 instead of reading from the weghts when there is a BN layer?
If bn
...
layer_conv.Bias = zeros(1,1,filters,'single'); % why it set to zero???
...
End

Hi Artem,

If you are not familiar with custom layers, I would suggest to use YOLOv2 that is officially supported by Computer Vision Toolbox for training.
https://jp.mathworks.com/help/deeplearning/examples/object-detection-using-yolo-v2.html

Darknet Importer can be used to import Darknet53 as an feature extractor for YOLOv2 network.

Dear Kei Otsuka,
Thank you very much for this code.
I try to train yolo but get the error
------------------------------------------------------------
Error using upsample2dLayer/predict
Method 'predict' is not defined for class 'upsample2dLayer' or is removed from MATLAB's search path.
----------------------------------------------------------
Would you suggest how to around it?

Hi Roberto,

Thank you for pointing it out.
Since TrainedMean is read-only parameter on 18a or earlier, can you use R2018b or later version?

I will update the information of release compatibility for this submission.

Hi
I tried to import a trained network by typing this command

[lgraph, anchors, classes] = importDarknetNetwork('yolov3-tiny-obj_3000.weights', 'yolov3-tiny-obj.cfg');

but I get this error

Darknet weights file version info
Major : 0 Minor : 1 Revision : 0
Loading cfg file
Creating graph of network layers for deep learing
--------------------------------------------------------------------
Image input layer : Height - 416 Width - 416 Channel - 3
2D conv layer 1 : Filtersize - 16 : Stride - 1 : Read param# - 432
You cannot set the read-only property 'TrainedMean' of BatchNormalizationLayer.
Error in createConv2d (line 109)
layer_bn.TrainedMean = bn_mean;
Error in importDarknetNetwork (line 86)
[lgraph, readSize] = createConv2d(lgraph, layerInfo, fid, i);

Could you suggest me how to solve it?

Dear @Kei Otsuka:
I'wil try ,thans very much! 非常感谢！

You can confirm both the number of total parameters and read parameters in command window. If the numbers match up, weights would be loaded successfully. Since YOLOv3-tiny makes prediction at two scales, two unused output would be expected after importing it into MATLAB.

when I load"yolov3-tiny.cfg","yolov3-tiny.weights", after analyzeNetwork , there are three errors,1, missing out put layer, 2,unused output "conv2d_10" ;3,unused output "conv2d_13", is't I successful load cfg and weights files ?

Thank you.
The code requires 'Deep Learning Toolbox Importer for TensorFlow-Keras Models' add-on. Please add it to the description.