Comments (12)
The paper is still under review. We plan to release the code within the next few months. Stay tuned!
from efficientps.
Dear All,
Thank you for your patience. We have received the first reviews for our EfficientPS paper and we are in the rebuttal phase at the moment. We will release the code after the final acceptance of the paper.
from efficientps.
Thank you for your patience. The paper is still under review. We hope to release the code by the end of the year.
from efficientps.
We still are waiting for the reviews but we will release the code by the end of this month.
from efficientps.
Code has been released
from efficientps.
how long?
from efficientps.
Any news about this?
from efficientps.
waiting for
from efficientps.
Marked
from efficientps.
Really excited to see the code. Heartiest thanks for doing such great work and we easily can use those model. Thank you
from efficientps.
Hi @avalada any updates?
from efficientps.
When?
from efficientps.
Related Issues (20)
- kitti subset HOT 2
- not find efficientPS_singlegpu_sample HOT 1
- run on KITTI dataset HOT 1
- Fail to open efficientPS_cityscapes.zip HOT 3
- pre-trained model (cityscape) classes missing? HOT 1
- Issue Inference of EfficientPS not working
- which dataset to download from Cityscapes download directory
- Installation of EfficientPS not working due to dependency inplace_abn (inplace_abn not updated for CUDA Driver Version 11) HOT 1
- Mask dislocation when test
- Convert the model to onnx HOT 3
- Could not download the pretrained models using the links in README HOT 1
- I get a bad kitti segmentation result with kitti pre-trained model. HOT 1
- How to compute mIoU based on results
- A bug about the cityscapes_demo.py
- problem with converting cityscapes HOT 1
- NameError: name ' samples_ per_ gpu' is not defined HOT 2
- Pre-Trained Models is not available
- RuntimeError: CUDA error: invalid device function,The cuda in the environment matches the cuda in mmdetection. Why the error HOT 1
- ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part. HOT 1
- Semantic segmentation metrics are used for evaluation
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