A simple consistency training framework for semi-supervised image semantic segmentation

Related tags

Deep Learningsemi-supervised-learningsegmentation
Overview

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

PseudoSeg is a simple consistency training framework for semi-supervised image semantic segmentation, which has a simple and novel re-design of pseudo-labeling to generate well-calibrated structured pseudo labels for training with unlabeled or weakly-labeled data. It is implemented by Yuliang Zou (research intern) in 2020 Summer.

This is not an official Google product.

Instruction

Installation

  • Use a virtual environment
virtualenv -p python3 --system-site-packages env
source env/bin/activate
  • Install packages
pip install -r requirements.txt

Dataset

Create a dataset folder under the ROOT directory, then download the pre-created tfrecords for voc12 and coco, and extract them in dataset folder. You may also want to check the filenames for each split under data_splits folder.

Training

NOTE:

  • We train all our models using 16 V100 GPUs.
  • The ImageNet pre-trained models can be download here.
  • For VOC12, ${SPLIT} can be 2_clean, 4_clean, 8_clean, 16_clean_3 (representing 1/2, 1/4, 1/8, and 1/16 splits), NUM_ITERATIONS should be set to 30000.
  • For COCO, ${SPLIT} can be 32_all, 64_all, 128_all, 256_all, 512_all (representing 1/32, 1/64, 1/128, 1/256, and 1/512 splits), NUM_ITERATIONS should be set to 200000.

Supervised baseline

python train_sup.py \
  --logtostderr \
  --train_split="${SPLIT}" \
  --model_variant="xception_65" \
  --atrous_rates=6 \
  --atrous_rates=12 \
  --atrous_rates=18 \
  --output_stride=16 \
  --decoder_output_stride=4 \
  --train_crop_size="513,513" \
  --num_clones=16 \
  --train_batch_size=64 \
  --training_number_of_steps="${NUM_ITERATIONS}" \
  --fine_tune_batch_norm=true \
  --tf_initial_checkpoint="${INIT_FOLDER}/xception_65/model.ckpt" \
  --train_logdir="${TRAIN_LOGDIR}" \
  --dataset_dir="${DATASET}"

PseudoSeg (w/ unlabeled data)

python train_wss.py \
  --logtostderr \
  --train_split="${SPLIT}" \
  --train_split_cls="train_aug" \
  --model_variant="xception_65" \
  --atrous_rates=6 \
  --atrous_rates=12 \
  --atrous_rates=18 \
  --output_stride=16 \
  --decoder_output_stride=4 \
  --train_crop_size="513,513" \
  --num_clones=16 \
  --train_batch_size=64 \
  --training_number_of_steps="${NUM_ITERATIONS}" \
  --fine_tune_batch_norm=true \
  --tf_initial_checkpoint="${INIT_FOLDER}/xception_65/model.ckpt" \
  --train_logdir="${TRAIN_LOGDIR}" \
  --dataset_dir="${DATASET}"

PseudoSeg (w/ image-level labeled data)

python train_wss.py \
  --logtostderr \
  --train_split="${SPLIT}" \
  --train_split_cls="train_aug" \
  --model_variant="xception_65" \
  --atrous_rates=6 \
  --atrous_rates=12 \
  --atrous_rates=18 \
  --output_stride=16 \
  --decoder_output_stride=4 \
  --train_crop_size="513,513" \
  --num_clones=16 \
  --train_batch_size=64 \
  --training_number_of_steps="${NUM_ITERATIONS}" \
  --fine_tune_batch_norm=true \
  --tf_initial_checkpoint="${INIT_FOLDER}/xception_65/model.ckpt" \
  --train_logdir="${TRAIN_LOGDIR}" \
  --dataset_dir="${DATASET}" \
  --weakly=true

Evaluation

NOTE: ${EVAL_CROP_SIZE} should be 513,513 for VOC12, 641,641 for COCO.

python eval.py \
  --logtostderr \
  --eval_split="val" \
  --model_variant="xception_65" \
  --atrous_rates=6 \
  --atrous_rates=12 \
  --atrous_rates=18 \
  --output_stride=16 \
  --decoder_output_stride=4 \
  --eval_crop_size="${EVAL_CROP_SIZE}" \
  --checkpoint_dir="${TRAIN_LOGDIR}" \
  --eval_logdir="${EVAL_LOGDIR}" \
  --dataset_dir="${DATASET}" \
  --max_number_of_evaluations=1

Visualization

NOTE: ${VIS_CROP_SIZE} should be 513,513 for VOC12, 641,641 for COCO.

python vis.py \
  --logtostderr \
  --vis_split="val" \
  --model_variant="xception_65" \
  --atrous_rates=6 \
  --atrous_rates=12 \
  --atrous_rates=18 \
  --output_stride=16 \
  --decoder_output_stride=4 \
  --vis_crop_size="${VIS_CROP_SIZE}" \
  --checkpoint_dir="${CKPT}" \
  --vis_logdir="${VIS_LOGDIR}" \
  --dataset_dir="${PASCAL_DATASET}" \
  --also_save_raw_predictions=true

Citation

If you use this work for your research, please cite our paper.

@article{zou2020pseudoseg,
  title={PseudoSeg: Designing Pseudo Labels for Semantic Segmentation},
  author={Zou, Yuliang and Zhang, Zizhao and Zhang, Han and Li, Chun-Liang and Bian, Xiao and Huang, Jia-Bin and Pfister, Tomas},
  journal={International Conference on Learning Representations (ICLR)},
  year={2021}
}
Owner
Google Interns
Google Interns
GitHub Repository
TOOD: Task-aligned One-stage Object Detection, ICCV2021 Oral

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of

264 Jan 09, 2023
Implementation for NeurIPS 2021 Submission: SparseFed

READ THIS FIRST This repo is an anonymized version of an existing repository of GitHub, for the AIStats 2021 submission: SparseFed: Mitigating Model P

2 Jun 15, 2022
Shared Attention for Multi-label Zero-shot Learning

Shared Attention for Multi-label Zero-shot Learning Overview This repository contains the implementation of Shared Attention for Multi-label Zero-shot

dathuynh 26 Dec 14, 2022
ML From Scratch

ML from Scratch MACHINE LEARNING TOPICS COVERED - FROM SCRATCH Linear Regression Logistic Regression K Means Clustering K Nearest Neighbours Decision

Tanishq Gautam 66 Nov 02, 2022
Implementation for paper LadderNet: Multi-path networks based on U-Net for medical image segmentation

Implementation for paper LadderNet: Multi-path networks based on U-Net for medical image segmentation This implementation is based on orobix implement

Juntang Zhuang 116 Sep 06, 2022
使用深度学习框架提取视频硬字幕；docker容器免安装深度学习库，使用本地api接口使得界面和后端识别分离；

extract-video-subtittle 使用深度学习框架提取视频硬字幕； 本地识别无需联网； CPU识别速度可观； 容器提供API接口； 运行环境 本项目运行环境非常好搭建，我做好了docker容器免安装各种深度学习包； 提供windows界面操作； 容器为CPU版本； 视频演示 https

歌者 16 Aug 06, 2022
Adversarial Learning for Modeling Human Motion

Adversarial Learning for Modeling Human Motion This repository contains the open source code which reproduces the results for the paper: Adversarial l

wangqi 6 Jun 15, 2021
A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science

PyGrid is a peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft. PyGrid is also the central serv

OpenMined 615 Jan 03, 2023
Detectron2 for Document Layout Analysis

Detectron2 trained on PubLayNet dataset This repo contains the training configurations, code and trained models trained on PubLayNet dataset using Det

Himanshu 163 Nov 21, 2022
Catalyst.Detection

Accelerated DL R&D PyTorch framework for Deep Learning research and development. It was developed with a focus on reproducibility, fast experimentatio

Catalyst-Team 12 Oct 25, 2021
QHack—the quantum machine learning hackathon

Official repo for QHack—the quantum machine learning hackathon

Xanadu 72 Dec 21, 2022
Accelerating BERT Inference for Sequence Labeling via Early-Exit

Sequence-Labeling-Early-Exit Code for ACL 2021 paper: Accelerating BERT Inference for Sequence Labeling via Early-Exit Requirement: Please refer to re

李孝男 23 Oct 14, 2022
PyTorch implementation of the paper:A Convolutional Approach to Melody Line Identification in Symbolic Scores.

Symbolic Melody Identification This repository is an unofficial PyTorch implementation of the paper:A Convolutional Approach to Melody Line Identifica

Sophia Y. Chou 3 Feb 21, 2022
vit for few-shot classification

Few-Shot ViT Requirements PyTorch (= 1.9) TorchVision timm (latest) einops tqdm numpy scikit-learn scipy argparse tensorboardx Pretrained Checkpoints

Martin Dong 26 Nov 30, 2022
Code for our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".

Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes (CVPR 2021) Project page | Paper | Colab | Colab for Drawing App Rethinking Style

CompVis Heidelberg 153 Jan 04, 2023
A python module for scientific analysis of 3D objects based on VTK and Numpy

A lightweight and powerful python module for scientific analysis and visualization of 3d objects.

Marco Musy 1.5k Jan 06, 2023
Predict and time series avocado hass

RECOMMENDER SYSTEM MARKETING TỔNG QUAN VỀ HỆ THỐNG DỮ LIỆU 1. Giới thiệu - Tiki là một hệ sinh thái thương mại "all in one", trong đó có tiki.vn, là

hieulmsc 3 Jan 10, 2022
A list of all named GANs!

The GAN Zoo Every week, new GAN papers are coming out and it's hard to keep track of them all, not to mention the incredibly creative ways in which re

Avinash Hindupur 12.9k Jan 08, 2023
OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework

OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework Introduction OpenFed is a foundational library for federated learning

25 Dec 12, 2022
A simple and useful implementation of LPIPS.

lpips-pytorch Description Developing perceptual distance metrics is a major topic in recent image processing problems. LPIPS[1] is a state-of-the-art

So Uchida 121 Dec 24, 2022