Person Re-identification

Last update: Jun 17, 2021

Related tags

Deep Learning hcmus-person-reid

Overview

Person Re-identification

Final project of Computer Vision

Table of content

Person Re-identification

Students:

Name	Student ID	Email
Nguyen Hoang Quan	18120145	[email protected]
Le Minh Khoa	18120415	[email protected]

Proposed method

Dataset

Market-1501

Dataset preparation

Download raw dataset

Download Market-1501 via Google Drive: link

Set dataset path

In config.json file, change root value to dataset path.

Train

Config

Check config.json file for training configuration.

Run

Run python main.py to start training.

Evaluation

Methods	Train feature	Test feature	Rank-1	Rank-5	mAP
Baseline	Global	Global	83.28	94.18	65.96
Baseline	Local	Local	80.20	92.76	59.66
Baseline	Global, Local	Global	84.14	93.97	67.58
Baseline	Global, Local	Local	86.37	95.10	68.68
Baseline + Identity Loss	Global, Local	Global	83.55	93.35	67.66
Baseline + Identity Loss	Global, Local	Local	84.23	93.62	63.99
Baseline + Identity Loss + Random Flip	Global, Local	Global	85.42	93.97	69.34
Baseline + Identity Loss + Random Flip	Global, Local	Local	86.82	94.89	67.59
Re-ranking	Global, Local	Global	87.47	92.96	82.90
Re-ranking + Identity Loss	Global, Local	Global	86.19	92.76	81.27
Re-ranking + Identity loss + Random Flip	Global, Local	Global	87.50	92.93	82.68

Person Re-identification

Related tags

Overview

Person Re-identification

Table of content

Students:

Proposed method

Dataset

Dataset preparation

Download raw dataset

Set dataset path

Train

Config

Run

Evaluation

Owner

Nguyễn Hoàng Quân

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Person Re-identification

Related tags

Overview

Person Re-identification

Table of content

Students:

Proposed method

Dataset

Dataset preparation

Download raw dataset

Set dataset path

Train

Config

Run

Evaluation

Owner

Nguyễn Hoàng Quân

Using Machine Learning to Create High-Res Fine Art

PyTorch implementations of algorithms for density estimation

Python Actor concurrency library

Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation

Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding

This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Computer Vision Paper Reviews with Key Summary of paper, End to End Code Practice and Jupyter Notebook converted papers

PyTorch implementation of the Crafting Better Contrastive Views for Siamese Representation Learning

The Body Part Regression (BPR) model translates the anatomy in a radiologic volume into a machine-interpretable form.

Pytorch implementations of popular off-policy multi-agent reinforcement learning algorithms, including QMix, VDN, MADDPG, and MATD3.

Pre-Trained Image Processing Transformer (IPT)

meProp: Sparsified Back Propagation for Accelerated Deep Learning (ICML 2017)

VLGrammar: Grounded Grammar Induction of Vision and Language

TSIT: A Simple and Versatile Framework for Image-to-Image Translation

A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

code for generating data set ES-ImageNet with corresponding training code

Chinese Mandarin tts text-to-speech 中文 (普通话) 语音 合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder,

A web application that provides real time temperature and humidity readings of a house.

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Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

Chinese Mandarin tts text-to-speech 中文 (普通话) 语音合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder,