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
Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization LPN [Paper] NEWs Prerequisites Python 3.6 GPU Memory = 8G Numpy 1.

46 Dec 14, 2022
Progressive Domain Adaptation for Object Detection

Progressive Domain Adaptation for Object Detection Implementation of our paper Progressive Domain Adaptation for Object Detection, based on pytorch-fa

96 Nov 25, 2022
A toolset of Python programs for signal modeling and indentification via sparse semilinear autoregressors.

SPAAR Description A toolset of Python programs for signal modeling via sparse semilinear autoregressors. References Vides, F. (2021). Computing Semili

Fredy Vides 0 Oct 30, 2021
Code for paper: "Spinning Language Models for Propaganda-As-A-Service"

Spinning Language Models for Propaganda-As-A-Service This is the source code for the Arxiv version of the paper. You can use this Google Colab to expl

Eugene Bagdasaryan 16 Jan 03, 2023
Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent

Narya The Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository

Paul Garnier 121 Dec 30, 2022
The code for our paper "AutoSF: Searching Scoring Functions for Knowledge Graph Embedding"

AutoSF The code for our paper "AutoSF: Searching Scoring Functions for Knowledge Graph Embedding" and this paper has been accepted by ICDE2020. News:

AutoML Research 64 Dec 17, 2022
Instantaneous Motion Generation for Robots and Machines.

Ruckig Instantaneous Motion Generation for Robots and Machines. Ruckig generates trajectories on-the-fly, allowing robots and machines to react instan

Berscheid 374 Dec 23, 2022
Parameter Efficient Deep Probabilistic Forecasting

PEDPF Parameter Efficient Deep Probabilistic Forecasting (PEDPF) is a repository containing code to run experiments for several deep learning based pr

Olivier Sprangers 10 Jun 13, 2022
Data, model training, and evaluation code for "PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models".

PubTables-1M This repository contains training and evaluation code for the paper "PubTables-1M: Towards a universal dataset and metrics for training a

Microsoft 365 Jan 04, 2023
Official implementation of FCL-taco2: Fast, Controllable and Lightweight version of Tacotron2 @ ICASSP 2021

FCL-Taco2: Towards Fast, Controllable and Lightweight Text-to-Speech synthesis (ICASSP 2021) Paper | Demo Block diagram of FCL-taco2, where the decode

Disong Wang 39 Sep 28, 2022
This repository contains the reference implementation for our proposed Convolutional CRFs.

ConvCRF This repository contains the reference implementation for our proposed Convolutional CRFs in PyTorch (Tensorflow planned). The two main entry-

Marvin Teichmann 553 Dec 07, 2022
Python framework for Stochastic Differential Equations modeling

SDElearn: a Python package for SDE modeling This package implements functionalities for working with Stochastic Differential Equations models (SDEs fo

4 May 10, 2022
Ganilla - Official Pytorch implementation of GANILLA

GANILLA We provide PyTorch implementation for: GANILLA: Generative Adversarial Networks for Image to Illustration Translation. Paper Arxiv Updates (Fe

Samet Hi 462 Dec 05, 2022
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (Findings of EMNLP 2021))

PTvsBT On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (Findings of EMNLP 2021) Citation Please cite a

Sunbow Liu 10 Nov 25, 2022
Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"

This repository contains code for the following two papers: VisualBERT: A Simple and Performant Baseline for Vision and Language (arxiv) with a short

Natural Language Processing @UCLA 463 Dec 09, 2022
Preprossing-loan-data-with-NumPy - In this project, I have cleaned and pre-processed the loan data that belongs to an affiliate bank based in the United States.

Preprossing-loan-data-with-NumPy In this project, I have cleaned and pre-processed the loan data that belongs to an affiliate bank based in the United

Dhawal Chitnavis 2 Jan 03, 2022
FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation

FIRA is a learning-based commit message generation approach, which first represents code changes via fine-grained graphs and then learns to generate commit messages automatically.

Van 21 Dec 30, 2022
A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)

EfficientNet PyTorch Quickstart Install with pip install efficientnet_pytorch and load a pretrained EfficientNet with: from efficientnet_pytorch impor

Luke Melas-Kyriazi 7.2k Jan 06, 2023
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"

Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng Internati

Princeton Vision & Learning Lab 115 Jan 04, 2023
[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdhury, Yongxin Yan

Ayan Kumar Bhunia 44 Dec 12, 2022