Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion Models

Overview

Label-Efficient Semantic Segmentation with Diffusion Models

Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion Models

This code is based on datasetGAN and guided-diffusion.

Note: use --recurse-submodules when clone.

 

Overview

The paper investigates the representations learned by the state-of-the-art DDPMs and shows that they capture high-level semantic information valuable for downstream vision tasks. We design a simple segmentation approach that exploits these representations and outperforms the alternatives in the few-shot operating point in the context of semantic segmentation.

DDPM-based Segmentation

 

Dependencies

  • Python >= 3.7
  • Packages: see requirements.txt

 

Datasets

The evaluation is performed on 6 collected datasets with a few annotated images in the training set: Bedroom-18, FFHQ-34, Cat-15, Horse-21, CelebA-19 and ADE-Bedroom-30. The number corresponds to the number of semantic classes.

datasets.tar.gz (~47Mb)

 

DDPM

Pretrained DDPMs

The models trained on LSUN are adopted from guided-diffusion. FFHQ-256 is trained by ourselves using the same model parameters as for the LSUN models.

LSUN-Bedroom: lsun_bedroom.pt
FFHQ-256: ffhq.pt
LSUN-Cat: lsun_cat.pt
LSUN-Horse: lsun_horse.pt

Run

  1. Download the datasets:
      bash datasets/download_datasets.sh
  2. Download the DDPM checkpoint:
       bash checkpoints/ddpm/download_checkpoint.sh
  3. Check paths in experiments/ /ddpm.json
  4. Run: bash scripts/ddpm/train_interpreter.sh

Available checkpoint names: lsun_bedroom, ffhq, lsun_cat, lsun_horse
Available dataset names: bedroom_28, ffhq_34, cat_15, horse_21, celeba_19, ade_bedroom_30

How to improve the performance

  1. Set input_activations=true in experiments/ /ddpm.json .
       In this case, the feature dimension is 18432.
  2. Tune for a particular task what diffusion steps and UNet blocks to use.

 

DatasetDDPM

Synthetic datasets

To download DDPM-produced synthetic datasets (50000 samples, ~7Gb):
bash synthetic-datasets/gan/download_synthetic_dataset.sh

Run | Option #1

  1. Download the synthetic dataset:
       bash synthetic-datasets/ddpm/download_synthetic_dataset.sh
  2. Check paths in experiments/ /datasetDDPM.json
  3. Run: bash scripts/datasetDDPM/train_deeplab.sh

Run | Option #2

  1. Download the datasets:
       bash datasets/download_datasets.sh

  2. Download the DDPM checkpoint:
       bash checkpoints/ddpm/download_checkpoint.sh

  3. Check paths in experiments/ /datasetDDPM.json

  4. Train an interpreter on a few DDPM-produced annotated samples:
       bash scripts/datasetDDPM/train_interpreter.sh

  5. Generate a synthetic dataset:
       bash scripts/datasetDDPM/generate_dataset.sh
        Please specify the hyperparameters in this script for the available resources.
        On 8xA100 80Gb, it takes about 12 hours to generate 10000 samples.

  6. Run: bash scripts/datasetDDPM/train_deeplab.sh
       One needs to specify the path to the generated data. See comments in the script.

Available checkpoint names: lsun_bedroom, ffhq, lsun_cat, lsun_horse
Available dataset names: bedroom_28, ffhq_34, cat_15, horse_21

 

SwAV

Pretrained SwAVs

We pretrain SwAV models using the official implementation on the LSUN and FFHQ-256 datasets:

LSUN-Bedroom: lsun_bedroom.pth
FFHQ-256: ffhq.pth
LSUN-Cat: lsun_cat.pth
LSUN-Horse: lsun_horse.pth

Training setup:

Dataset epochs batch-size multi-crop num-prototypes
LSUN 200 1792 2x256 + 6x108 1000
FFHQ-256 400 2048 2x224 + 6x96 200

Run

  1. Download the datasets:
       bash datasets/download_datasets.sh
  2. Download the SwAV checkpoint:
       bash checkpoints/swav/download_checkpoint.sh
  3. Check paths in experiments/ /swav.json
  4. Run: bash scripts/swav/train_interpreter.sh

Available checkpoint names: lsun_bedroom, ffhq, lsun_cat, lsun_horse
Available dataset names: bedroom_28, ffhq_34, cat_15, horse_21, celeba_19, ade_bedroom_30

 

DatasetGAN

Opposed to the official implementation, more recent StyleGAN2(-ADA) models are used.

Synthetic datasets

To download GAN-produced synthetic datasets (50000 samples):

bash synthetic-datasets/gan/download_synthetic_dataset.sh

Run

Since we almost fully adopt the official implementation, we don't provide our reimplementation here. However, one can still reproduce our results:

  1. Download the synthetic dataset:
      bash synthetic-datasets/gan/download_synthetic_dataset.sh
  2. Change paths in experiments/ /datasetDDPM.json
  3. Change paths and run: bash scripts/datasetDDPM/train_deeplab.sh

Available dataset names: bedroom_28, ffhq_34, cat_15, horse_21

 

Results

  • Performance in terms of mean IoU:
Method Bedroom-28 FFHQ-34 Cat-15 Horse-21 CelebA-19 ADE-Bedroom-30
ALAE 20.0 ± 1.0 48.1 ± 1.3 -- -- 49.7 ± 0.7 15.0 ± 0.5
VDVAE -- 57.3 ± 1.1 -- -- 54.1 ± 1.0 --
GAN Inversion 13.9 ± 0.6 51.7 ± 0.8 21.4 ± 1.7 17.7 ± 0.4 51.5 ± 2.3 11.1 ± 0.2
GAN Encoder 22.4 ± 1.6 53.9 ± 1.3 32.0 ± 1.8 26.7 ± 0.7 53.9 ± 0.8 15.7 ± 0.3
SwAV 41.0 ± 2.3 54.7 ± 1.4 44.1 ± 2.1 51.7 ± 0.5 53.2 ± 1.0 30.3 ± 1.5
DatasetGAN 31.3 ± 2.7 57.0 ± 1.0 36.5 ± 2.3 45.4 ± 1.4 -- --
DatasetDDPM 46.9 ± 2.8 56.0 ± 0.9 45.4 ± 2.8 60.4 ± 1.2 -- --
DDPM 46.1 ± 1.9 57.0 ± 1.4 52.3 ± 3.0 63.1 ± 0.9 57.0 ± 1.0 32.3 ± 1.5

 

  • Examples of segmentation masks predicted by the DDPM-based method:
DDPM-based Segmentation

 

Cite

@misc{baranchuk2021labelefficient,
      title={Label-Efficient Semantic Segmentation with Diffusion Models}, 
      author={Dmitry Baranchuk and Ivan Rubachev and Andrey Voynov and Valentin Khrulkov and Artem Babenko},
      year={2021},
      eprint={2112.03126},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Owner
Yandex Research
Yandex Research
TensorFlow implementation of original paper : https://github.com/hszhao/PSPNet

Keras implementation of PSPNet(caffe) Implemented Architecture of Pyramid Scene Parsing Network in Keras. For the best compability please use Python3.

VladKry 386 Dec 29, 2022
Synthetic LiDAR sequential point cloud dataset with point-wise annotations

SynLiDAR dataset: Learning From Synthetic LiDAR Sequential Point Cloud This is official repository of the SynLiDAR dataset. For technical details, ple

78 Dec 27, 2022
The official TensorFlow implementation of the paper Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition

Action Transformer A Self-Attention Model for Short-Time Human Action Recognition This repository contains the official TensorFlow implementation of t

PIC4SeRCentre 20 Jan 03, 2023
SIR model parameter estimation using a novel algorithm for differentiated uniformization.

TenSIR Parameter estimation on epidemic data under the SIR model using a novel algorithm for differentiated uniformization of Markov transition rate m

The Spang Lab 4 Nov 30, 2022
A pytorch implementation of faster RCNN detection framework (Use detectron2, it's a masterpiece)

Notice(2019.11.2) This repo was built back two years ago when there were no pytorch detection implementation that can achieve reasonable performance.

Ruotian(RT) Luo 1.8k Jan 01, 2023
[BMVC'21] Official PyTorch Implementation of Grounded Situation Recognition with Transformers

Grounded Situation Recognition with Transformers Paper | Model Checkpoint This is the official PyTorch implementation of Grounded Situation Recognitio

Junhyeong Cho 18 Jul 19, 2022
PyTorch implementation of CVPR'18 - Perturbative Neural Networks

This is an attempt to reproduce results in Perturbative Neural Networks paper. See original repo for details.

Michael Klachko 57 May 14, 2021
Fast and customizable reconnaissance workflow tool based on simple YAML based DSL.

Fast and customizable reconnaissance workflow tool based on simple YAML based DSL, with support of notifications and distributed workload of that work

Américo Júnior 3 Mar 11, 2022
Self-supervised Label Augmentation via Input Transformations (ICML 2020)

Self-supervised Label Augmentation via Input Transformations Authors: Hankook Lee, Sung Ju Hwang, Jinwoo Shin (KAIST) Accepted to ICML 2020 Install de

hankook 96 Dec 29, 2022
"SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image [Paper] [Website] Pipeline Code Environment pip install -r requirements

VITA 250 Jan 05, 2023
一个目标检测的通用框架(不需要cuda编译),支持Yolo全系列(v2~v5)、EfficientDet、RetinaNet、Cascade-RCNN等SOTA网络。

一个目标检测的通用框架(不需要cuda编译),支持Yolo全系列(v2~v5)、EfficientDet、RetinaNet、Cascade-RCNN等SOTA网络。

Haoyu Xu 203 Jan 03, 2023
TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning

TransZero++ This repository contains the testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" submitted

Shiming Chen 6 Aug 16, 2022
DM-ACME compatible implementation of the Arm26 environment from Mujoco

ACME-compatible implementation of Arm26 from Mujoco This repository contains a customized implementation of Mujoco's Arm26 model, that can be used wit

1 Dec 24, 2021
EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow

EfficientDet This is an implementation of EfficientDet for object detection on Keras and Tensorflow. The project is based on the official implementati

1.3k Dec 19, 2022
Impelmentation for paper Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing

FGHV Impelmentation for paper Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing Requirements Python 3.6 Pytorch 1.5.0 Cud

5 Jun 02, 2022
Repository containing the PhD Thesis "Formal Verification of Deep Reinforcement Learning Agents"

Getting Started This repository contains the code used for the following publications: Probabilistic Guarantees for Safe Deep Reinforcement Learning (

Edoardo Bacci 5 Aug 31, 2022
LIMEcraft: Handcrafted superpixel selectionand inspection for Visual eXplanations

LIMEcraft LIMEcraft: Handcrafted superpixel selectionand inspection for Visual eXplanations The LIMEcraft algorithm is an explanatory method based on

MI^2 DataLab 4 Aug 01, 2022
A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization

University1652-Baseline [Paper] [Slide] [Explore Drone-view Data] [Explore Satellite-view Data] [Explore Street-view Data] [Video Sample] [中文介绍] This

Zhedong Zheng 335 Jan 06, 2023
Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport This GitHub page provides code for reproducing the results i

Andrew Zammit Mangion 1 Nov 08, 2021
Implementation of FitVid video prediction model in JAX/Flax.

FitVid Video Prediction Model Implementation of FitVid video prediction model in JAX/Flax. If you find this code useful, please cite it in your paper:

Google Research 62 Nov 25, 2022