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About pretrained model #7

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ohosh opened this issue Jun 29, 2022 · 7 comments
Open

About pretrained model #7

ohosh opened this issue Jun 29, 2022 · 7 comments

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@ohosh
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ohosh commented Jun 29, 2022

Dear author,

Thanks for the sharing code.

when I use the pretrained model about Kitti result you gave to run the datasets, the results can not reach the effect in your article. I don't know if there is a problem with the model I used, because there are multiple models in the file. I would like to ask which model you used to get the results in your article.

Thanks!

@huixiancheng
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Owner

Hi. The KITTI results only contain our training logs and results under 64x512 inputs.
The model under the file named “512-594” you can get a test accuracy of 59.4.
The file named “512+vaild-607” is the result after adding the validation set to the training and fine-tuning it, using this model test you should get a test set accuracy of 60.7 as reported in our paper.
Very sorry, pre-trained models and logs under larger inputs may not be considered for release

@cardwing
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cardwing commented Jul 6, 2022

@ohosh @huixiancheng I have trained the CENet which can achieve 65 mIoU on SemanticKITTI test set with 64x512 input resolution. The trained model will be put in https://github.com/cardwing/Codes-for-PVKD.

@cardwing
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cardwing commented Jul 6, 2022

More powerful range-image-based models built on the awesome CENet will also be put in that repo (70+ mIoU on SemanticKITTI test set).

@huixiancheng
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@cardwing Incredible. 😮 😱 🙈 That's awesome and amazing work.
I think this will drive further development of the range-based methods. 👍👍👍
Looking forward to the release.

@cardwing
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cardwing commented Jul 6, 2022

The CENet with 64x512 input resolution has been uploaded to https://github.com/cardwing/Codes-for-PVKD. The reproduced performance (67.6% mIoU) is much higher than the reported value on SemanticKITTI test set.

@huixiancheng
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Owner

Glad to hear this. 👏 👍
Modify README to point to that great work and Repo. 👉

@huishuai13
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The CENet with 64x512 input resolution has been uploaded to https://github.com/cardwing/Codes-for-PVKD. The reproduced performance (67.6% mIoU) is much higher than the reported value on SemanticKITTI test set.

Is the CENet model with 64x512 in your repository trained with distillation?

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