tensorflow-yolo-v3
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mystic123
Mistake in your version of tiny yolo v3
Hi,
I am studying you implementation of yolo_v3-tiny. Thanks for your job !
But it seems that you have introduced an error compared to the original network.
According to the Darknet's config file : here The last MaxPooling layer has a size of [2, 2] with stride of 1.
But your network has only maxpool layer has a size of [2, 2] with stride of 2 !!
So, the tensor resized by the upsampling layer is not of shape [13, 13, 128] like in Darknet, but is [6, 6, 128]. And I am pretty sure that it is really impacting performance of recognition.
Could you please fix this issue ? (I'm not really comfortable with Slim, so I can't do the modification by myself).
Do you think this modification is sufficient:
changing line 62 of yolo_v3_tiny.py :
from :
inputs = slim.max_pool2d(inputs, [2, 2], scope='pool2')
to
if i < 5:
inputs = slim.max_pool2d(inputs, [2, 2], scope='pool2')
else:
inputs = slim.max_pool2d(inputs, [2, 2], stride=1, padding="SAME", scope='pool2')
@LucasMahieu sorry for late response. Yes, this modification looks good. Can you submit pull request with this change?
Before we close the issue, There are more mistakes. The following log is from darknet. This use yolov3-tiny.cfg.
layer filters size input output
0 conv 16 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BFLOPs
1 max 2 x 2 / 2 416 x 416 x 16 -> 208 x 208 x 16
2 conv 32 3 x 3 / 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BFLOPs
3 max 2 x 2 / 2 208 x 208 x 32 -> 104 x 104 x 32
4 conv 64 3 x 3 / 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BFLOPs
5 max 2 x 2 / 2 104 x 104 x 64 -> 52 x 52 x 64
6 conv 128 3 x 3 / 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BFLOPs
7 max 2 x 2 / 2 52 x 52 x 128 -> 26 x 26 x 128
8 conv 256 3 x 3 / 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BFLOPs
9 max 2 x 2 / 2 26 x 26 x 256 -> 13 x 13 x 256
10 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
11 max 2 x 2 / 1 13 x 13 x 512 -> 13 x 13 x 512
12 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
13 conv 256 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BFLOPs
14 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
15 conv 255 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BFLOPs
16 yolo
17 route 13
18 conv 128 1 x 1 / 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BFLOPs
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8
21 conv 256 3 x 3 / 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BFLOPs
22 conv 255 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BFLOPs
23 yolo
Max pool no.11 has stride 1 which currently awaiting fix from pool request.
EDIT : I'm sorry, I did the mistake reporting that both conv no.15 and conv no.22 are missing. Both are actually included in _detection_layer() function because both have linear activation function. @LucasMahieu You can unlike me, man. XD
The fix of the stride for the 5th max pooling (11th layer) is pushed.
Pull request is pending .
@i3oi3o Thanks for your edit.
HI there. Based on the formula, shouldn't a 2x2/1 Maxpool result in the dimensions reducing by 1 on both axes? I have been trying my own implementation of v3 tiny and I have been getting the following output tensor shapes.
torch.Size([1, 16, 416, 416]) torch.Size([1, 16, 208, 208]) torch.Size([1, 32, 208, 208]) torch.Size([1, 32, 104, 104]) torch.Size([1, 64, 104, 104]) torch.Size([1, 64, 52, 52]) torch.Size([1, 128, 52, 52]) torch.Size([1, 128, 26, 26]) torch.Size([1, 256, 26, 26]) torch.Size([1, 256, 13, 13]) torch.Size([1, 512, 13, 13]) torch.Size([1, 512, 12, 12]) torch.Size([1, 1024, 12, 12]) torch.Size([1, 256, 12, 12]) torch.Size([1, 512, 12, 12]) torch.Size([1, 255, 12, 12]) torch.Size([1, 432, 85]) torch.Size([1, 256, 12, 12]) torch.Size([1, 128, 12, 12]) torch.Size([1, 128, 24, 24])
