Are the output dimensions of the first and second convolutional layer in YOLO paper correct?

Question

I was reading the last version of the YOLO paper available in Arxiv, and I don't fully understand the output dimensions (I understand width and height, but not depth) of the first and second convolutional layers.

Shouldn't the output of the first layer be 112x112x64? Shouldn't the output of the second layer be 56x56x192? I thought (and this is the case after the 3rd layer) that the depth of the ouput of a conv layer is equal to the number of filters of this layer. This is why after the first conv layer (that contains 64 filters) I would expect an output depth of 64. And the same for the second conv layer that contains 192 filters, I would expect the output to have a depth of 192.

Answer 1

In any case anyone is struggling with the same problem. It seems that they were simply typos in the original paper. I have downloaded the author's framework Darknet, as well as the configuration and weight files for YOLOv1.

Then, the architecture can be tested with one sample image using this command:

./darknet yolo test cfg/yolov1/yolo.cfg yolov1.weights data/person.jpg

The output of this command also prints the full architecure of the net. There, I see the expected depth values after the first (depth of 64) and second max layers (depth of 192):

   layer   filters  size/strd(dil)      input                output
       0 Create CUDA-stream - 0 
     Create cudnn-handle 0 
    conv     64       7 x 7/ 2    448 x 448 x   3 ->  224 x 224 x  64 0.944 BF
       1 max                2x 2/ 2    224 x 224 x  64 ->  112 x 112 x  64 0.003 BF
       2 conv    192       3 x 3/ 1    112 x 112 x  64 ->  112 x 112 x 192 2.775 BF
       3 max                2x 2/ 2    112 x 112 x 192 ->   56 x  56 x 192 0.002 BF

So it seems clear to me that the 192 and 256 depth values after the first and second max layer on the figure in the question are just typos.