jd730 / eopsn Goto Github PK

How to solve the problem of insufficient video memory? Thanks!

Hi @jd730, thanks for sharing your wonderful work.
In the README, you said the training requires "void-suppression" pre-trained model. However, I find that in the config file, you still use the standard pre-trained model here.
If I want to train your EOPSN, which pre-trained model should I use?
Thx.

Hi,
thanks for providing the code to your method. I am interested in trying this out on another dataset, namely cityscapes. I already know that I would have to adapt the dataloading to cityscapes, what I am still unsure about is which pretrained model I would need.

In this issue you mention that training needs a model pretrained with void-supression, the weights of which you provide. How would I go about pretraining such a model on cityscapes? Is there code in this repository for pretraining on COCO, which I could adapt to cityscapes?

请问是否有训练好的模型能直接让我们跑测试？

Hi,

Thanks for the interesting paper and open-sourced code.

Recently, I ran the EOPSN method on K20 setting folllowing the given guideline (w/o any editing) and I found the results of unknown things are quite different from the reported one.

From the table, it seems that the released code achieves a much better improvement than the reported one. However, when I further inspect the predictions of class-wise unkown things, it seems that EOPSN's unkown recognition is dominated by the "car" class and other unkown classes are rarely detected. Moreover, the reproduced results may not support the visualization results in Fig5 since several unkown classes are shown to be detected, e.g., stop sign, keyboard, banana, and toilet. So, could you please release the EOPSN checkpoint which supports the reported results? Thanks a lot.

BTW, I found that the training of EOPSN requires the pre-trained model of Void-Suppression, but the current released codebase only contains the void-train method. I wonder could you please release the void-suppression code for better reproduction? Thanks again.

FYI, the predictions of class-wise unkown things on Void-Suppression method are as follows and the results are identical to the reported ones in the paper

When running the training, I noticed that sometimes some losses (especially the box regression loss) can become Infinity. This seems to happen especially once many exemplars have been mined. I was wondering if you encountered this issue as well, and if you know of any fixes.