TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Related tags

Deep LearningTransFGU
Overview

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li, Rong Jin

[Preprint]

Getting Started

Create the environment

# create conda env
conda create -n TransFGU python=3.8
# activate conda env
conda activate TransFGU
# install pytorch
conda install pytorch=1.8 torchvision cudatoolkit=10.1
# install other dependencies
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.0/index.html
pip install -r requirements.txt

Dataset Preparation

the structure of dataset folders should be as follow:

data/
    │── MSCOCO/
    │     ├── images/
    │     │     ├── train2017/
    │     │     └── val2017/
    │     └── annotations/
    │           ├── train2017/
    │           ├── val2017/
    │           ├── instances_train2017.json
    │           └── instances_val2017.json
    │── Cityscapes/
    │     ├── leftImg8bit/
    │     │     ├── train/
    │     │     │       ├── aachen
    │     │     │       └── ...
    │     │     └──── val/
    │     │             ├── frankfurt
    │     │             └── ...
    │     └── gtFine/
    │           ├── train/
    │           │       ├── aachen
    │           │       └── ...
    │           └──── val/
    │                   ├── frankfurt
    │                   └── ...
    │── PascalVOC/
    │     ├── JPEGImages/
    │     ├── SegmentationClass/
    │     └── ImageSets/
    │           └── Segmentation/
    │                   ├── train.txt
    │                   └── val.txt
    └── LIP/
          ├── train_images/
          ├── train_segmentations/
          ├── val_images/
          ├── val_segmentations/
          ├── train_id.txt
          └── val_id.txt

Model download

Name mIoU Pixel Accuracy Model
COCOStuff-27 16.19 44.52 Google Drive
COCOStuff-171 11.93 34.32 Google Drive
COCO-80 12.69 64.31 Google Drive
Cityscapes 16.83 77.92 Google Drive
Pascal-VOC 37.15 83.59 Google Drive
LIP-5 25.16 65.76 Google Drive
LIP-16 15.49 60.08 Google Drive
LIP-19 12.24 42.52 Google Drive

Train and Evaluate Our Method

To train and evaluate our method on different datasets under desired granularity level, please follow the instructions here.

Citation

If you find our work useful in your research, please consider citing:

@article{yin2021transfgu,
  title={TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation},
  author={Zhaoyun, Yin and Pichao, Wang and Fan, Wang and Xianzhe, Xu and Hanling, Zhang and Hao, Li and Rong, Jin},
  journal={arXiv preprint arXiv:2112.01515},
  year={2021}
}

LICENSE

The code is released under the MIT license.

Copyright

Copyright (C) 2010-2021 Alibaba Group Holding Limited.

Owner
DamoCV
CV team of DAMO academy
DamoCV
Facial Image Inpainting with Semantic Control

Facial Image Inpainting with Semantic Control In this repo, we provide a model for the controllable facial image inpainting task. This model enables u

Ren Yurui 8 Nov 22, 2021
Deep Crop Rotation

Deep Crop Rotation Paper (to come very soon!) We propose a deep learning approach to modelling both inter- and intra-annual patterns for parcel classi

Félix Quinton 5 Sep 23, 2022
tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

timeseriesAI 2.8k Jan 08, 2023
Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device" @ CAD&Graphics2019

PortraitNet Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device". @ CAD&Graphics 2019 Introduction We propose a

265 Dec 01, 2022
State-Relabeling Adversarial Active Learning

State-Relabeling Adversarial Active Learning Code for SRAAL [2020 CVPR Oral] Requirements torch = 1.6.0 numpy = 1.19.1 tqdm = 4.31.1 AL Results The

10 Jul 14, 2022
PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning"

PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning".

Berivan Isik 8 Dec 08, 2022
Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution

Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution Abstract Within the Latin (and ancient Greek) production, it is well

4 Dec 03, 2022
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.

Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021. Bobo Xi, Jiaojiao Li, Yunsong Li and Qian Du. Code f

Bobo Xi 7 Nov 03, 2022
Semantic Segmentation with Pytorch-Lightning

This is a simple demo for performing semantic segmentation on the Kitti dataset using Pytorch-Lightning and optimizing the neural network by monitoring and comparing runs with Weights & Biases.

Boris Dayma 58 Nov 18, 2022
Distributed Asynchronous Hyperparameter Optimization in Python

Hyperopt: Distributed Hyperparameter Optimization Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which

6.5k Jan 01, 2023
Model Zoo of BDD100K Dataset

Model Zoo of BDD100K Dataset

ETH VIS Group 200 Dec 27, 2022
A Java implementation of the experiments for the paper "k-Center Clustering with Outliers in Sliding Windows"

OutliersSlidingWindows A Java implementation of the experiments for the paper "k-Center Clustering with Outliers in Sliding Windows" Dataset generatio

PaoloPellizzoni 0 Jan 05, 2022
Official repository for the ICCV 2021 paper: UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model.

UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model Official repository for the ICCV 2021 paper: UltraPose: Syn

MomoAILab 92 Dec 21, 2022
DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe.

DeepLab Introduction DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe. It combines densely-compute

Ali 234 Nov 14, 2022
Deep Learning Training Scripts With Python

Deep Learning Training Scripts DNN Frameworks Caffe PyTorch Tensorflow CNN Models VGG ResNet DenseNet Inception Language Modeling GatedCNN-LM Attentio

Multicore Computing Research Lab 16 Dec 15, 2022
A framework for multi-step probabilistic time-series/demand forecasting models

JointDemandForecasting.py A framework for multi-step probabilistic time-series/demand forecasting models File stucture JointDemandForecasting contains

Stanford Intelligent Systems Laboratory 3 Sep 28, 2022
PAWS 🐾 Predicting View-Assignments with Support Samples

This repo provides a PyTorch implementation of PAWS (predicting view assignments with support samples), as described in the paper Semi-Supervised Learning of Visual Features by Non-Parametrically Pre

Facebook Research 437 Dec 23, 2022
This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

Intro This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales Vehicle Sam

39 Jul 21, 2022
A project studying the influence of communication in multi-objective normal-form games

Communication in Multi-Objective Normal-Form Games This repo consists of five different types of agents that we have used in our study of communicatio

Willem Röpke 0 Dec 17, 2021
Guiding evolutionary strategies by (inaccurate) differentiable robot simulators @ NeurIPS, 4th Robot Learning Workshop

Guiding Evolutionary Strategies by Differentiable Robot Simulators In recent years, Evolutionary Strategies were actively explored in robotic tasks fo

Vladislav Kurenkov 4 Dec 14, 2021