Pyramid Scene Parsing Network, CVPR2017.

Related tags

Deep LearningPSPNet
Overview

Pyramid Scene Parsing Network

by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page.

Introduction

This repository is for 'Pyramid Scene Parsing Network', which ranked 1st place in ImageNet Scene Parsing Challenge 2016. The code is modified from Caffe version of DeepLab v2 and yjxiong for evaluation. We merge the batch normalization layer named 'bn_layer' in the former one into the later one while keep the original 'batch_norm_layer' in the later one unchanged for compatibility. The difference is that 'bn_layer' contains four parameters as 'slope,bias,mean,variance' while 'batch_norm_layer' contains two parameters as 'mean,variance'. Several evaluation code is borrowed from MIT Scene Parsing.

PyTorch Version

Highly optimized PyTorch codebases available for semantic segmentation in repo: semseg, including full training and testing codes for PSPNet and PSANet.

Installation

For installation, please follow the instructions of Caffe and DeepLab v2. To enable cuDNN for GPU acceleration, cuDNN v4 is needed. If you meet error related with 'matio', please download and install matio as required in 'DeepLab v2'.

The code has been tested successfully on Ubuntu 14.04 and 12.04 with CUDA 7.0.

Usage

  1. Clone the repository:

    git clone https://github.com/hszhao/PSPNet.git
  2. Build Caffe and matcaffe:

    cd $PSPNET_ROOT
    cp Makefile.config.example Makefile.config
    vim Makefile.config
    make -j8 && make matcaffe
  3. Evaluation:

    • Evaluation code is in folder 'evaluation'.
    • Download trained models and put them in folder 'evaluation/model':
    • Modify the related paths in 'eval_all.m':
      • Mainly variables 'data_root' and 'eval_list', and your image list for evaluation should be similarity to that in folder 'evaluation/samplelist' if you use this evaluation code structure.
      • Matlab 'parfor' evaluation is used and the default GPUs are with ID [0:3]. Modify variable 'gpu_id_array' if needed. We assume that number of images can be divided by number of GPUs; if not, you can just pad your image list or switch to single GPU evaluation by set 'gpu_id_array' be length of one, and change 'parfor' to 'for' loop.
    cd evaluation
    vim eval_all.m
    • Run the evaluation scripts:
    ./run.sh
    
  4. Results:

    Prediction results will show in folder 'evaluation/mc_result' and the expected scores are:

    (single scale testing denotes as 'ss' and multiple scale testing denotes as 'ms')

    • PSPNet50 on ADE20K valset (mIoU/pAcc): 41.68/80.04 (ss) and 42.78/80.76 (ms)
    • PSPNet101 on VOC2012 testset (mIoU): 85.41 (ms)
    • PSPNet101 on cityscapes valset (mIoU/pAcc): 79.70/96.38 (ss) and 80.91/96.59 (ms)
  5. Demo video:

    Video processed by PSPNet101 on cityscapes dataset:

    Merge with colormap on side: Video1

    Alpha blending with value as 0.5: Video2

Citation

If PSPNet is useful for your research, please consider citing:

@inproceedings{zhao2017pspnet,
  title={Pyramid Scene Parsing Network},
  author={Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya},
  booktitle={CVPR},
  year={2017}
}

Questions

Please contact '[email protected]'

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

causal-bald | Abstract | Installation | Example | Citation | Reproducing Results DUE An implementation of the methods presented in Causal-BALD: Deep B

OATML 13 Oct 07, 2022
This is a computer vision based implementation of the popular childhood game 'Hand Cricket/Odd or Even' in python

Hand Cricket Table of Content Overview Installation Game rules Project Details Future scope Overview This is a computer vision based implementation of

Abhinav R Nayak 6 Jan 12, 2022
Implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

CrossViT : Cross-Attention Multi-Scale Vision Transformer for Image Classification This is an unofficial PyTorch implementation of CrossViT: Cross-Att

Rishikesh (ऋषिकेश) 103 Nov 25, 2022
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Zhao Jian 3.1k Jan 02, 2023
Music Classification: Beyond Supervised Learning, Towards Real-world Applications

Music Classification: Beyond Supervised Learning, Towards Real-world Applications

104 Dec 15, 2022
Python package for downloading ECMWF reanalysis data and converting it into a time series format.

ecmwf_models Readers and converters for data from the ECMWF reanalysis models. Written in Python. Works great in combination with pytesmo. Citation If

TU Wien - Department of Geodesy and Geoinformation 31 Dec 26, 2022
Official Code for "Non-deep Networks"

Non-deep Networks arXiv:2110.07641 Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Overview: Depth is the hallmark of DNNs. But more depth m

Ankit Goyal 567 Dec 12, 2022
Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting

Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting #Dataset The folder "Dataset" contains the dataset use in this work and m

0 Jan 08, 2022
Code and dataset for ACL2018 paper "Exploiting Document Knowledge for Aspect-level Sentiment Classification"

Aspect-level Sentiment Classification Code and dataset for ACL2018 [paper] ‘‘Exploiting Document Knowledge for Aspect-level Sentiment Classification’’

Ruidan He 146 Nov 29, 2022
Aircraft design optimization made fast through modern automatic differentiation

Aircraft design optimization made fast through modern automatic differentiation. Plug-and-play analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.

Peter Sharpe 394 Dec 23, 2022
Personals scripts using ageitgey/face_recognition

HOW TO USE pip3 install requirements.txt Add some pictures of known people in the folder 'people' : a) Create a folder called by the name of the perso

Antoine Bollengier 1 Jan 06, 2022
PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection?

PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

Toyota Research Institute - Machine Learning 364 Dec 27, 2022
PyTorch implementation(s) of various ResNet models from Twitch streams.

pytorch-resnet-twitch PyTorch implementation(s) of various ResNet models from Twitch streams. Status: ResNet50 currently not working. Will update in n

Daniel Bourke 3 Jan 11, 2022
Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification tasks

Uniformer - Pytorch Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification ta

Phil Wang 90 Nov 24, 2022
A tool for calculating distortion parameters in coordination complexes.

OctaDist Octahedral distortion calculator: A tool for calculating distortion parameters in coordination complexes. https://octadist.github.io/ Registe

OctaDist 12 Oct 04, 2022
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm

100 Dec 28, 2022
It is a simple library to speed up CLIP inference up to 3x (K80 GPU)

CLIP-ONNX It is a simple library to speed up CLIP inference up to 3x (K80 GPU) Usage Install clip-onnx module and requirements first. Use this trick !

Gerasimov Maxim 93 Dec 20, 2022
Framework for abstracting Amiga debuggers and access to AmigaOS libraries and devices.

Framework for abstracting Amiga debuggers. This project provides abstration to control an Amiga remotely using a debugger. The APIs are not yet stable

Roc Vallès 39 Nov 22, 2022
Code for "Adversarial attack by dropping information." (ICCV 2021)

AdvDrop Code for "AdvDrop: Adversarial Attack to DNNs by Dropping Information(ICCV 2021)." Human can easily recognize visual objects with lost informa

Ranjie Duan 52 Nov 10, 2022
TransferNet: Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

TransferNet: Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network Created by Seunghoon Hong, Junhyuk Oh,

42 Jun 29, 2022