mj129 / coanet Goto Github PK

Hi,

Thanks for the great work.

Can you please release the script to perform "PreProcess SpaceNet Dataset"?

Thanks!

I have followed this work for a long time, and the result claims in the paper can't repeat!!
I have several qustions about the code and paper:

1. As we know, before the data feed into model, we need do some 'transfoms'. But why do you padding the ignore region for 0 and then using it for loss calculation? And why not mask this region out?
2. About the Connction Branch, in your process code, "create_connction.py", we can see that, it create from the GroundTruth using some pixel interval. In the paper, page 7, it claimed that "Besides, we devise an upper bound to our proposed method CoANet by using the ground truth of connectivity branch during inference". It doesn't make sense! CoANet-UB is so big just because It get the GroundTruth!
3. Go on with the Connction Branch, It claimed that "The upper bound results indicate that there is still large room for the connectivity branch to improve, and one possibility is to use a larger pixel interval in the future work."(page 7 right) But from the Ablation Study, TABLE V, page 10, we can see that It is not work by using a larger pixel interval. It is not consistent about your discussion.

Could you answer my qustion?

运行test.py的时候出现错误
Traceback (most recent call last):
File "test.py", line 6, in
from dataloaders import custom_transforms as tr
File "/data0/wangxue/CoANet-main/dataloaders/init.py", line 3, in
from prefetch_generator import BackgroundGenerator
ModuleNotFoundError: No module named 'prefetch_generator'

感谢您的出色工作和代码！我正在尝试复现您的算法，但不知道如何使用deepglobe数据集上的预测结果来进行APLS的计算，想请问您是如何将预测结果的.png图像转换为APLS计算所需的.csv格式的呢？再次感谢您的贡献，期待您的回复！

In Figure 4 of the paper, it looks to me like the diagram has left out some items.
I've drawn what i think is missing in red. Can you please correct me if this is wrong?

https://imgur.com/a/leR5lpT

找不到数据集，数据集如何下载

Hi, I'm doing research on raod extraction from remote sensing images recently. And I sincerely appreciate your works and suprised at remarkable perfomrance improvement comparing with previous achitectures. And I want to know more details on the experiments done in your paper as following:

1. The experiment results are obtained from the partitioned and resized validation dataset? Or from the whole dataset which also retained the original size?
2. When you calculated the training and interference time of those models, did you maintain the same batch size of images?
3. Did you use the same data augmentation and post processing published in other model's codes when you test the other model's performance?
4. What kind of skeletonize function is used when you compute Apls metric. Could you publish the codes or name the function you used?

Sincerely hope that you could solve my confusions!

I'm unable to load the pretrained model file that you have provided. Can you please check if the file is correctly uploaded or not?
I'm getting the following error while loading the model:

[enforce fail at inline_container.cc:144] . PytorchStreamReader failed reading zip archive: failed finding central directory

will look forward to your response. Thanks.

Hello, thanks for the excellent work.