Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Related tags

Deep Learning NRD_decoder
Overview

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Requirements

This repository needs mmsegmentation

Training

To train the model(s) in the paper, run this command:

python tools/train.py ./configs/NRD/ade20k/NRD_r101_512x512_164k_ade20k.py

The batch size is 16 in this work. Please change the 'samples_per_gpu' in configs/base/datasets/.. accordingly

Evaluation

To evaluate my model at single-scale inference, run:

python tools/eval.py ./configs/NRD/ade20k/NRD_r101_512x512_164k_ade20k.py  {path-to-checkpoint-file}   --eval mIoU

Pre-trained Models

Results

Our model achieves the following performance on :

[Semantic segmentation results]

Model name datasets mIoU mIoU (ms)
NRD-r101 ade20k (val) 44.01 45.62
NRD-x101 ade20k (val) 44.34 46.35
NRD-r101 pascal-context(val) 52.31 (59 classes) 54.1 (59 classes)
NRD-r101 pascal-context(val) 47.5 (60 classes) 40.9 (60 classes)
NRD-r50 Cityscapes (val) 79.8 80.8
NRD-r101 Cityscapes (val) 80.7 82.0

Contributing

The code is mostly taken from mmsegmentation mmsegmentation is released under the Apache 2.0 license.

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Comments
  • CVE-2007-4559 Patch

    CVE-2007-4559 Patch

    Patching CVE-2007-4559

    Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

    If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

    opened by TrellixVulnTeam 0
Owner
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