CENet
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huixiancheng
About pretrained model
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！
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
@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.
More powerful range-image-based models built on the awesome CENet will also be put in that repo (70+ mIoU on SemanticKITTI test set).
@cardwing Incredible. :open_mouth: :scream: :see_no_evil: That's awesome and amazing work. I think this will drive further development of the range-based methods. :thumbsup::thumbsup::thumbsup: Looking forward to the release.
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.
Glad to hear this. :clap: :thumbsup: Modify README to point to that great work and Repo. :point_right:
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?
