As per the documentation of yolov2layers, the Input Argument imageSize should be specified as one of these values:
- Two-element vector of form [H W] - For a grayscale image of size H-by-W
- Three-element vector of form [H W 3] - For an RGB color image of size H-by-W
Along with the above information the documentation page of yolov2layers says that
"yolov2Layers uses a pretrained neural network as the base network to which it adds a detection subnetwork required for creating a YOLO v2 object detection network. Given a base network, yolov2Layers removes all the layers succeeding the feature layer in the base network and adds the detection subnetwork. The detection subnetwork comprises of groups of serially connected convolution, ReLU, and batch normalization layers. The YOLO v2 transform layer and YOLO v2 output layer are added to the detection subnetwork. If you specify the name-value pair 'ReorgLayerSource', the YOLO v2 network concatenates the output of reorganization layer with the output of feature layer." And you can refer to creating a custom YOLO v2 network layer-by-layer at Create YOLO v2 Object Detection Network.
Hence in order to use the network for grayscale input the pretrained network which is given to the yolov2layers function should also take grayscale input i.e, it's imageInputLayer must have the number of channels as 1. As a workaround you can make use of the ColorPreprocessing property of the augmentedImageDatastore.
