Knowledge Distillation Toolbox for Semantic Segmentation

Related tags

Deep Learning SegDistill
Overview

SegDistill: Toolbox for Knowledge Distillation on Semantic Segmentation Networks

This repo contains the supported code and configuration files for SegDistill .It is based on mmsegmentaion.

Installation

conda create -n mmcv python=3.8 -y
conda activate mmcv

pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

pip install mmcv-full==1.2.2 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html

pip install future tensorboard
pip install IPython
pip install attr
pip install timm

git clone https://github.com/wzpscott/SegDistill.git -b main
cd SegDistill
pip install -e .

Prepare Data

We conducted experiments on ADE20k dataset. The training and validation set of ADE20K could be download from this link. Test set can be download from here. After downloading the dataset, you need to arrange the structure of your dataset like:

mmsegmentation
├── mmseg
├── tools
├── configs
├── data
│   ├── ade
│   │   ├── ADEChallengeData2016
│   │   │   ├── annotations
│   │   │   │   ├── training
│   │   │   │   ├── validation
│   │   │   ├── images
│   │   │   │   ├── training
│   │   │   │   ├── validation
│   ├── ...

See here for more instructions on data preparation.

Prepare Models

We provide links to pretrained weights of models used in the paper.

Model Pretrained on ImageNet-1K Trained on ADE20k
Segformer link link
Swin-Transformer link link
PSPNet link link

Write configs for semantic segmentaion KD

We use mmcv-fashion configs to control the KD process.

Run an example config with the following command:

 bash tools/dist_train.sh distillation_configs/example_config.py {num_gpu}

See here for detailed instructions for custom KD process on various network architectures.

Channel Group Distillation

Our Channel Group Distillation (CGD) considers a more extensive range of correlations inthe activation map and works well fortransformer structures than previous KD methods.

Comparison to Other KD methods

Comparison to Other KD methods

Results on ADE20k

Qualitative segmentation results on ADE20k produced from Segformer B0: (a) raw images, (b) ground truth (GT), (c) outputof the original student model (d) Channel-wise Distillation (CD) and (e) Channel Group Distillation(CGD) Qualitative segmentation results

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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
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GitHub
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