eigencontours's People
eigencontours's Issues
what the means of train_seg.pkl and how to generate it?
1
About visualize U
Eigencontours/Instance_segmentation/code_v1_COCO/tools/SVD.py
Lines 98 to 128 in 0efb0fd
In line 107:U = self.U[:, k:k + 1] * 3000
What's the reason for multiplying by 3000 here? (I drew the image as a centre ray in my own dataset based on this code, and I'm wondering what could be the cause of the error)
Hi, what direction does this paper belong to and how should I search for papers in this direction
Question on F measure computation
Hi,
It seems that the F measure is computed by using batch_f_measure
provided by davisinteractive:
Eigencontours/Preprocessing/code_v1_COCO/libs/S3_convert.py
Lines 107 to 111 in 24be95d
But when I run the code, my F measure results are really high. For M=8
, the average F score on COCO2017 of my experiments is around 0.84, which is much higher than that of around 0.4 reported in the paper (see Fig. 8 (c)).
In particular, even with M=1
, the metric is very high. As a specific example, the following display imgs (left is the approximate mask with cfg.dim=1
, right is the ground-truth mask) have a computed batch_f_measure
of 1.0. I think there is something wrong since the two masks are so different.
More related questions:
-
I take the SVD of the full contour matrix computed over COCO2017 train, whose size is
360 x 557905
, and during the convert step, I apply it to COCO2017 val. Although the paper does not mention how to use train/val, I assume this is the correct way. Can you please confirm this? -
The paper mentions to compute the F measure, "bipartite matching is performed between the boundary points of a ground-truth contour and its approximated version" and then "F score is defined as the harmonic mean of the precision (P) and the recall (R) of the matching results". In the code this is done by using
batch_f_measure
provided by davisinteractive between the ground truth masks and approximate masks. Is this equivalent to the way described in the paper?
Looking forward to your reply. Thank you very much!
Link not working
When will the rest part of the code be released?
Very insightful work! I am wondering when will the code for KINS dataset and SBD dataset be released as that for COCO?
Also, is there any plan of releasing code for instance segmentation by embedding eigencontours in YOLOv3?
Thank you so much!
Eigencontours only computed over 40k subsamples?
Hi, I found your work is very interesting! I have a quick question. It seems that the eigencontours for COCO is computed only over 40000 subsamples based on the following line in the code:
However, in the paper it says that "the proposed eigencontours are determined for all instances in all categories in a training dataset". Since 40k < 110k (size of COCO), which is not "all instances", I wonder which one is correct?
Thanks!
When will the code be open source
.....
Great job. When will the code be released
Very much looking forward
hope to see the rest code of instance segmentation
Great work, but I'm still a little confused。 M can control the shape of the object, but how to determine the size and position of the object, so I'd love to learn the rest of the code
How do I generate train_seg.pickle and train_bb.pickle for a custom dataset
The pkl file is required to perform the first step of preprocessing
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