Semi-supevised Semantic Segmentation with High- and Low-level Consistency

Related tags

Deep Learningsemisup-semseg
Overview

Semi-supevised Semantic Segmentation with High- and Low-level Consistency

This Pytorch repository contains the code for our work Semi-supervised Semantic Segmentation with High- and Low-level Consistency. The approach uses two network branches that link semi-supervised classification with semi-supervised segmentation including self-training. The approach attains significant improvement over existing methods, especially when trained with very few labeled samples. On several standard benchmarks - PASCAL VOC 2012,PASCAL-Context, and Cityscapes - the approach achieves new state-of-the-art in semi-supervised learning.

We propose a two-branch approach to the task of semi-supervised semantic segmentation. The lower branch predicts pixel-wise class labels and is referred to as the Semi-Supervised Semantic Segmentation GAN(s4GAN). The upper branch performs image-level classification and is denoted as the Multi-Label Mean Teacher(MLMT).

Here, this repository contains the source code for the s4GAN branch. MLMT branch is adapted from Mean-Teacher work for semi-supervised classification. Instructions for setting up the MLMT branch are given below.

Package pre-requisites

The code runs on Python 3 and Pytorch 0.4 The following packages are required.

pip install scipy tqdm matplotlib numpy opencv-python

Dataset preparation

Download ImageNet pretrained Resnet-101(Link) and place it ./pretrained_models/

PASCAL VOC

Download the dataset(Link) and extract in ./data/voc_dataset/

PASCAL Context

Download the annotations(Link) and extract in ./data/pcontext_dataset/

Cityscapes

Download the dataset from the Cityscapes dataset server(Link). Download the files named 'gtFine_trainvaltest.zip', 'leftImg8bit_trainvaltest.zip' and extract in ./data/city_dataset/

Training and Validation on PASCAL-VOC Dataset

Results in the paper are averaged over 3 random splits. Same splits are used for reporting baseline performance for fair comparison.

Training fully-supervised Baseline (FSL)

python train_full.py    --dataset pascal_voc  \
                        --checkpoint-dir ./checkpoints/voc_full \
                        --ignore-label 255 \
                        --num-classes 21

Training semi-supervised s4GAN (SSL)

python train_s4GAN.py   --dataset pascal_voc  \
                        --checkpoint-dir ./checkpoints/voc_semi_0_125 \
                        --labeled-ratio 0.125 \
                        --ignore-label 255 \ 
                        --num-classes 21

Validation

python evaluate.py --dataset pascal_voc  \
                   --num-classes 21 \
                   --restore-from ./checkpoints/voc_semi_0_125/VOC_30000.pth

Training MLMT Branch

python train_mlmt.py \
        --batch-size-lab 16 \
        --batch-size-unlab 80 \
        --labeled-ratio 0.125 \
        --exp-name voc_semi_0_125_MLMT \
        --pkl-file ./checkpoints/voc_semi_0_125/train_voc_split.pkl

Final Evaluation S4GAN + MLMT

python evaluate.py --dataset pascal_voc  \
                   --num-classes 21 \
                   --restore-from ./checkpoints/voc_semi_0_125/VOC_30000.pth \
                   --with-mlmt \
                   --mlmt-file ./mlmt_output/voc_semi_0_125_MLMT/output_ema_raw_100.txt

Training and Validation on PASCAL-Context Dataset

python train_full.py    --dataset pascal_context  \
                        --checkpoint-dir ./checkpoints/pc_full \
                        --ignore-label -1 \
                        --num-classes 60

python train_s4GAN.py  --dataset pascal_context  \
                       --checkpoint-dir ./checkpoints/pc_semi_0_125 \
                       --labeled-ratio 0.125 \
                       --ignore-label -1 \
                       --num-classes 60 \
                       --split-id ./splits/pc/split_0.pkl
                       --num-steps 60000

python evaluate.py     --dataset pascal_context  \
                       --num-classes 60 \
                       --restore-from ./checkpoints/pc_semi_0_125/VOC_40000.pth

Training and Validation on Cityscapes Dataset

python train_full.py    --dataset cityscapes \
                        --checkpoint-dir ./checkpoints/city_full_0_125 \
                        --ignore-label 250 \
                        --num-classes 19 \
                        --input-size '256,512'  

python train_s4GAN.py   --dataset cityscapes \
                        --checkpoint-dir ./checkpoints/city_semi_0_125 \
                        --labeled-ratio 0.125 \
                        --ignore-label 250 \
                        --num-classes 19 \
                        --split-id ./splits/city/split_0.pkl \
                        --input-size '256,512' \
                        --threshold-st 0.7 \
                        --learning-rate-D 1e-5 

python evaluate.py      --dataset cityscapes \
                        --num-classes 19 \
                        --restore-from ./checkpoints/city_semi_0_125/VOC_30000.pth

Acknowledgement

Parts of the code have been adapted from: DeepLab-Resnet-Pytorch, AdvSemiSeg, PyTorch-Encoding

Citation

@ARTICLE{8935407,
  author={S. {Mittal} and M. {Tatarchenko} and T. {Brox}},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Semi-Supervised Semantic Segmentation With High- and Low-Level Consistency}, 
  year={2021},
  volume={43},
  number={4},
  pages={1369-1379},
  doi={10.1109/TPAMI.2019.2960224}}
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