Below is an image of the YOLO network 
for object detection. The first layer says Conv. Layer 7x7x64-s-2. To the best of my knowledge, it means a convolution layer using a kernel of size 4x4 with 64 output channels and stride 2. But the picture shows 192
output channels at this layer. Am I understanding something wrong or is it a typo in the paper? The second layer has the same porblem while the others don't.
Understanding this illustration for the YOLO network
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1 Answers
We get 64 output channels per each input channel in the image. Since each image is a colored one, it has 3 channels corresponding to red, green, and blue. Hence we get 64 x 3 = 192 output channels.
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