Code for the paper 'A High Performance CRF Model for Clothes Parsing'.

Overview

Clothes Parsing

Overview

This code provides an implementation of the research paper:

  A High Performance CRF Model for Clothes Parsing
  Edgar Simo-Serra, Sanja Fidler, Francesc Moreno-Noguer, and Raquel Urtasun
  Asian Conference on Computer Vision (ACCV), 2014

The code here allows training and testing of a model that got state-of-the-art results on the Fashionista dataset at the time of publication.

License

  Copyright (C) <2014> <Edgar Simo-Serra, Sanja Fidler, Francesc Moreno-Noguer, Raquel Urtasun>

  This work is licensed under the Creative Commons
  Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy
  of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or
  send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

  Edgar Simo-Serra, Institut de Robotica i Informatica Industrial (CSIC/UPC), December 2014.
  [email protected], http://www-iri.upc.es/people/esimo/

Installation

In order to get started first checkout out the source code and then extract the features:

# Check out the git and cd into it as working directory
git clone https://github.com/bobbens/clothes_parsing.git
cd clothes_parsing
# Get and unpack the necessary features
wget http://hi.cs.waseda.ac.jp/~esimo//data/poseseg.tar.bz2
tar xvjf poseseg.tar.bz2 

The dSP dependency must also be compiled. This can be done by:

cd lib/dSP_5.1
make # First edit the Makefile if necessary

Usage

You can reproduce results simply by running from Matlab:

sm = segmodel( 'PROFILE', '0.16', 'use_real_pose', false ); % Load the model, parameters can be set here
sm = sm.train_misc_unaries(); % Trains some misc stuff
sm = sm.train_MRF(); % Actually sets up and trains the CRF
R = sm.test_MRF_segmentation() % Performs testing and outputs results

This should generate an output like:

 BUILDING MRF OUTPUT 29 CLASSES (REAL POSE=0)...
 UNARIES:
    bgbias
    logreg:       29
    cpmc_logreg:  29
    cpmc
    shapelets
 HIGHER ORDER
    similarity
    limbs
 Initializing Image 011 / 350...   0.4 seconds!   

 ...

 Tested MRF in 319.0 seconds
 350 / 350... 

 R = 

     confusion: [29x29 double]
     order: [29x1 double]
     acc: 0.8432
     pre: [29x1 double]
     rec: [29x1 double]
     f1: [29x1 double]
     voc: [29x1 double]
     avr_pre: 0.3007
     avr_rec: 0.3292
     avr_f1: 0.3039
     avr_voc: 0.2013

Please note that due to stochastic components and differences between software versions, the numbers will not be exactly the same as the paper. For the paper all results were obtained on a linux machine running Ubuntu 12.04 with Matlab R2012a (7.14.0.739) 64-bit (glnxa64).

You can furthermore visualize the output of the model with:

sm.test_MRF_visualize( 'output/' )

This will save both the ground truth segmentations and the predicted segmentations in the directory 'output/' as shown in the paper.

If you use this code please cite:

 @InProceedings{SimoSerraACCV2014,
    author = {Edgar Simo-Serra and Sanja Fidler and Francesc Moreno-Noguer and Raquel Urtasun},
    title = {{A High Performance CRF Model for Clothes Parsing}},
    booktitle = "Proceedings of the Asian Conference on Computer Vision (2014)",
    year = 2014
 }

Acknowledgments

We would like to give our thanks to Kota Yamaguchi for his excellent code which we have used as a base for our model.

The different codes we have used (in alphabetical order):

Changelog

December 2014: Initial version 1.0 release

PyTorch-Multi-Style-Transfer - Neural Style and MSG-Net

PyTorch-Style-Transfer This repo provides PyTorch Implementation of MSG-Net (ours) and Neural Style (Gatys et al. CVPR 2016), which has been included

Hang Zhang 906 Jan 04, 2023
Official implementation of VaxNeRF (Voxel-Accelearated NeRF).

VaxNeRF Paper | Google Colab This is the official implementation of VaxNeRF (Voxel-Accelearated NeRF). VaxNeRF provides very fast training and slightl

naruya 132 Nov 21, 2022
5 Jan 05, 2023
The implementation of DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Dis

Microsoft 1.2k Jan 06, 2023
Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!

Serpent.AI - Game Agent Framework (Python) Update: Revival (May 2020) Development work has resumed on the framework with the aim of bringing it into 2

Serpent.AI 6.4k Jan 05, 2023
Implementation of light baking system for ray tracing based on Activision's UberBake

Vulkan Light Bakary MSU Graphics Group Student's Diploma Project Treefonov Andrey [GitHub] [LinkedIn] Project Goal The goal of the project is to imple

Andrey Treefonov 7 Dec 27, 2022
QTool: A Low-bit Quantization Toolbox for Deep Neural Networks in Computer Vision

This project provides abundant choices of quantization strategies (such as the quantization algorithms, training schedules and empirical tricks) for quantizing the deep neural networks into low-bit c

Monash Green AI Lab 51 Dec 10, 2022
A Demo server serving Bert through ONNX with GPU written in Rust with <3

Demo BERT ONNX server written in rust This demo showcase the use of onnxruntime-rs on BERT with a GPU on CUDA 11 served by actix-web and tokenized wit

Xavier Tao 28 Jan 01, 2023
Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

TDEER (WIP) Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021) Overview TDEER is an e

Alipay 6 Dec 17, 2022
Automatic caption evaluation metric based on typicality analysis.

SeMantic and linguistic UndeRstanding Fusion (SMURF) Automatic caption evaluation metric described in the paper "SMURF: SeMantic and linguistic UndeRs

Joshua Feinglass 6 Jan 09, 2022
Source code of our TTH paper: Targeted Trojan-Horse Attacks on Language-based Image Retrieval.

Targeted Trojan-Horse Attacks on Language-based Image Retrieval Source code of our TTH paper: Targeted Trojan-Horse Attacks on Language-based Image Re

fine 7 Aug 23, 2022
PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

halo 368 Dec 06, 2022
Face recognize system

FRS Face_recognize_system This project contains my work that target on solving some problems of FRS: Face detection: Retinaface Face anti-spoofing: Fo

Tran Anh Tuan 4 Nov 18, 2021
A PyTorch-based library for fast prototyping and sharing of deep neural network models.

A PyTorch-based library for fast prototyping and sharing of deep neural network models.

78 Jan 03, 2023
A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.

PokeGAN A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon. Dataset The model has been trained on dataset that includes 8

19 Jul 26, 2022
Classification models 1D Zoo - Keras and TF.Keras

Classification models 1D Zoo - Keras and TF.Keras This repository contains 1D variants of popular CNN models for classification like ResNets, DenseNet

Roman Solovyev 12 Jan 06, 2023
Official repository for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'21, Oral Presentation)

Official PyTorch Implementation for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'2021, Oral Presentation) HOTR: End-to-

Kakao Brain 114 Nov 28, 2022
Official PyTorch implementation of "The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation" (ICCV 21).

CenterGroup This the official implementation of our ICCV 2021 paper The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person P

Dynamic Vision and Learning Group 43 Dec 25, 2022
Evolving neural network parameters in JAX.

Evolving Neural Networks in JAX This repository holds code displaying techniques for applying evolutionary network training strategies in JAX. Each sc

Trevor Thackston 6 Feb 12, 2022
Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space"

MotionCLIP Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space". Please visit our webpage for mor

Guy Tevet 173 Dec 26, 2022