[Update] One-vs-Rest and One-vs-One for Multi-Class Classification | vsone – Vietnamnhanvan

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Last Updated on April 27, 2021

Not all classification predictive models support multi-class classification.

Algorithms such as the Perceptron, Logistic Regression, and Support Vector Machines were designed for binary classification and do not natively support classification tasks with more than two classes.

One approach for using binary classification algorithms for multi-classification problems is to split the multi-class classification dataset into multiple binary classification datasets and fit a binary classification model on each. Two different examples of this approach are the One-vs-Rest and One-vs-One strategies.

In this tutorial, you will discover One-vs-Rest and One-vs-One strategies for multi-class classification.

After completing this tutorial, you will know:

  • Binary classification models like logistic regression and SVM do not support multi-class classification natively and require meta-strategies.
  • The One-vs-Rest strategy splits a multi-class classification into one binary classification problem per class.
  • The One-vs-One strategy splits a multi-class classification into one binary classification problem per each pair of classes.

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How to Use One-vs-Rest and One-vs-One for Multi-Class Classification

Tutorial Overview

This tutorial is divided into three parts; they are:

  1. Binary Classifiers for Multi-Class Classification
  2. One-Vs-Rest for Multi-Class Classification
  3. One-Vs-One for Multi-Class Classification

Binary Classifiers for Multi-Class Classification

Classification is a predictive modeling problem that involves assigning a class label to an example.

Binary classification are those tasks where examples are assigned exactly one of two classes. Multi-class classification is those tasks where examples are assigned exactly one of more than two classes.

  • Binary Classification: Classification tasks with two classes.
  • Multi-class Classification: Classification tasks with more than two classes.

Some algorithms are designed for binary classification problems. Examples include:

  • Logistic Regression
  • Perceptron
  • Support Vector Machines

As such, they cannot be used for multi-class classification tasks, at least not directly.

Instead, heuristic methods can be used to split a multi-class classification problem into multiple binary classification datasets and train a binary classification model each.

Two examples of these heuristic methods include:

  • One-vs-Rest (OvR)
  • One-vs-One (OvO)

Let’s take a closer look at each.

One-Vs-Rest for Multi-Class Classification

One-vs-rest (OvR for short, also referred to as One-vs-All or OvA) is a heuristic method for using binary classification algorithms for multi-class classification.

It involves splitting the multi-class dataset into multiple binary classification problems. A binary classifier is then trained on each binary classification problem and predictions are made using the model that is the most confident.

For example, given a multi-class classification problem with examples for each class ‘red,’ ‘blue,’ and ‘green‘. This could be divided into three binary classification datasets as follows:

  • Binary Classification Problem 1: red vs [blue, green]
  • Binary Classification Problem 2: blue vs [red, green]
  • Binary Classification Problem 3: green vs [red, blue]

A possible downside of this approach is that it requires one model to be created for each class. For example, three classes requires three models. This could be an issue for large datasets (e.g. millions of rows), slow models (e.g. neural networks), or very large numbers of classes (e.g. hundreds of classes).

The obvious approach is to use a one-versus-the-rest approach (also called one-vs-all), in which we train C binary classifiers, fc(x), where the data from class c is treated as positive, and the data from all the other classes is treated as negative.

— Page 503, Machine Learning: A Probabilistic Perspective, 2012.

This approach requires that each model predicts a class membership probability or a probability-like score. The argmax of these scores (class index with the largest score) is then used to predict a class.

This approach is commonly used for algorithms that naturally predict numerical class membership probability or score, such as:

  • Logistic Regression
  • Perceptron

As such, the implementation of these algorithms in the scikit-learn library implements the OvR strategy by default when using these algorithms for multi-class classification.

We can demonstrate this with an example on a 3-class classification problem using the LogisticRegression algorithm. The strategy for handling multi-class classification can be set via the “multi_class” argument and can be set to “ovr” for the one-vs-rest strategy.

The complete example of fitting a logistic regression model for multi-class classification using the built-in one-vs-rest strategy is listed below.

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# logistic regression for multi-class classification using built-in one-vs-rest

from

sklearn

.

datasets

import

make_classification

from

sklearn

.

linear_model

import

LogisticRegression

# define dataset

X

,

y

=

make_classification

(

n_samples

=

1000

,

n_features

=

10

,

n_informative

=

5

,

n_redundant

=

5

,

n_classes

=

3

,

random_state

=

1

)

# define model

model

=

LogisticRegression

(

multi_class

=

‘ovr’

)

# fit model

model

.

fit

(

X

,

y

)

# make predictions

yhat

=

model

.

predict

(

X

)

The scikit-learn library also provides a separate OneVsRestClassifier class that allows the one-vs-rest strategy to be used with any classifier.

This class can be used to use a binary classifier like Logistic Regression or Perceptron for multi-class classification, or even other classifiers that natively support multi-class classification.

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It is very easy to use and requires that a classifier that is to be used for binary classification be provided to the OneVsRestClassifier as an argument.

The example below demonstrates how to use the OneVsRestClassifier class with a LogisticRegression class used as the binary classification model.

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# logistic regression for multi-class classification using a one-vs-rest

from

sklearn

.

datasets

import

make_classification

from

sklearn

.

linear_model

import

LogisticRegression

from

sklearn

.

multiclass

import

OneVsRestClassifier

# define dataset

X

,

y

=

make_classification

(

n_samples

=

1000

,

n_features

=

10

,

n_informative

=

5

,

n_redundant

=

5

,

n_classes

=

3

,

random_state

=

1

)

# define model

model

=

LogisticRegression

(

)

# define the ovr strategy

ovr

=

OneVsRestClassifier

(

model

)

# fit model

ovr

.

fit

(

X

,

y

)

# make predictions

yhat

=

ovr

.

predict

(

X

)

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One-Vs-One for Multi-Class Classification

One-vs-One (OvO for short) is another heuristic method for using binary classification algorithms for multi-class classification.

Like one-vs-rest, one-vs-one splits a multi-class classification dataset into binary classification problems. Unlike one-vs-rest that splits it into one binary dataset for each class, the one-vs-one approach splits the dataset into one dataset for each class versus every other class.

For example, consider a multi-class classification problem with four classes: ‘red,’ ‘blue,’ and ‘green,’ ‘yellow.’ This could be divided into six binary classification datasets as follows:

  • Binary Classification Problem 1: red vs. blue
  • Binary Classification Problem 2: red vs. green
  • Binary Classification Problem 3: red vs. yellow
  • Binary Classification Problem 4: blue vs. green
  • Binary Classification Problem 5: blue vs. yellow
  • Binary Classification Problem 6: green vs. yellow

This is significantly more datasets, and in turn, models than the one-vs-rest strategy described in the previous section.

The formula for calculating the number of binary datasets, and in turn, models, is as follows:

  • (NumClasses * (NumClasses – 1)) / 2

We can see that for four classes, this gives us the expected value of six binary classification problems:

  • (NumClasses * (NumClasses – 1)) / 2
  • (4 * (4 – 1)) / 2
  • (4 * 3) / 2
  • 12 / 2
  • 6

Each binary classification model may predict one class label and the model with the most predictions or votes is predicted by the one-vs-one strategy.

An alternative is to introduce K(K − 1)/2 binary discriminant functions, one for every possible pair of classes. This is known as a one-versus-one classifier. Each point is then classified according to a majority vote amongst the discriminant functions.

— Page 183, Pattern Recognition and Machine Learning, 2006.

Similarly, if the binary classification models predict a numerical class membership, such as a probability, then the argmax of the sum of the scores (class with the largest sum score) is predicted as the class label.

Classically, this approach is suggested for support vector machines (SVM) and related kernel-based algorithms. This is believed because the performance of kernel methods does not scale in proportion to the size of the training dataset and using subsets of the training data may counter this effect.

The support vector machine implementation in the scikit-learn is provided by the SVC class and supports the one-vs-one method for multi-class classification problems. This can be achieved by setting the “decision_function_shape” argument to ‘ovo‘.

The example below demonstrates SVM for multi-class classification using the one-vs-one method.

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# SVM for multi-class classification using built-in one-vs-one

from

sklearn

.

datasets

import

make_classification

from

sklearn

.

svm

import

SVC

# define dataset

X

,

y

=

make_classification

(

n_samples

=

1000

,

n_features

=

10

,

n_informative

=

5

,

n_redundant

=

5

,

n_classes

=

3

,

random_state

=

1

)

# define model

model

=

SVC

(

decision_function_shape

=

‘ovo’

)

# fit model

model

.

fit

(

X

,

y

)

# make predictions

yhat

=

model

.

predict

(

X

)

The scikit-learn library also provides a separate OneVsOneClassifier class that allows the one-vs-one strategy to be used with any classifier.

This class can be used with a binary classifier like SVM, Logistic Regression or Perceptron for multi-class classification, or even other classifiers that natively support multi-class classification.

It is very easy to use and requires that a classifier that is to be used for binary classification be provided to the OneVsOneClassifier as an argument.

The example below demonstrates how to use the OneVsOneClassifier class with an SVC class used as the binary classification model.

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# SVM for multi-class classification using one-vs-one

from

sklearn

.

datasets

import

make_classification

from

sklearn

.

svm

import

SVC

from

sklearn

.

multiclass

import

OneVsOneClassifier

# define dataset

X

,

y

=

make_classification

(

n_samples

=

1000

,

n_features

=

10

,

n_informative

=

5

,

n_redundant

=

5

,

n_classes

=

3

,

random_state

=

1

)

# define model

model

=

SVC

(

)

# define ovo strategy

ovo

=

OneVsOneClassifier

(

model

)

# fit model

ovo

.

fit

(

X

,

y

)

# make predictions

yhat

=

ovo

.

predict

(

X

)

Further Reading

This section provides more resources on the topic if you are looking to go deeper.

Books

APIs

Articles

Summary

In this tutorial, you discovered One-vs-Rest and One-vs-One strategies for multi-class classification.

Specifically, you learned:

  • Binary classification models like logistic regression and SVM do not support multi-class classification natively and require meta-strategies.
  • The One-vs-Rest strategy splits a multi-class classification into one binary classification problem per class.
  • The One-vs-One strategy splits a multi-class classification into one binary classification problem per each pair of classes.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

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[NEW] ibeis | vsone – Vietnamnhanvan

This project is a component of the WildMe / WildBook project: See https://github.com/WildbookOrg/

IBEIS – Image Analysis

I.B.E.I.S. = Image Based Ecological Information System

Program Description

IBEIS program for the storage and management of images and derived data for
use in computer vision algorithms. It aims to compute who an animal is, what
species an animal is, and where an animal is with the ultimate goal being to
ask important why biological questions. This This repo Image Analysis image
analysis module of IBEIS. It is both a python module and standalone program.

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Currently the system is build around and SQLite database, a PyQt4 GUI, and
matplotlib visualizations. Algorithms employed are: random forest species
detection and localization, hessian-affine keypoint detection, SIFT keypoint
description, LNBNN identification using approximate nearest neighbors.
Algorithms in development are SMK (selective match kernel) for identification
and deep neural networks for detection and localization.

The core of IBEIS is the IBEISController class. It provides an API into IBEIS
data management and algorithms. The IBEIS API Documentation can be found here:
http://erotemic.github.io/ibeis

The IBEIS GUI (graphical user interface) is built on top of the API.
We are also experimenting with a new web frontend that bypasses the older GUI code.

## Self Installing Executables:

Unfortunately we have not released self-installing-executables for IBEIS yet.
We plan to release these “soon”.

However there are old HotSpotter (the software which IBEIS is based on)
binaries available. These can be downloaded from: http://cs.rpi.edu/hotspotter/

Visual Demo

Match Scoring

Spatial Verification

python -m vtool.spatial_verification --test-spatially_verify_kpts --show

Name Scoring

python -m ibeis.algo.hots.chip_match show_single_namematch --qaid 

1

--show

Identification Ranking

python -m ibeis.algo.hots.chip_match show_ranked_matches --show --qaid 

86

Inference

# broken # python -m ibeis.algo.preproc.preproc_encounter compute_encounter_groups --show

Internal Modules

In the interest of modular code we are actively developing several different modules.

Erotemic’s IBEIS Image Analysis module dependencies

  • https://github.com/Erotemic/utool
  • https://github.com/Erotemic/plottool_ibeis
  • https://github.com/Erotemic/vtool_ibeis
  • https://github.com/Erotemic/guitool_ibeis
  • https://github.com/Erotemic/pyflann_ibeis
  • https://github.com/Erotemic/hesaff
  • https://github.com/Erotemic/futures_actors

bluemellophone’s IBEIS Image Analysis modules

  • https://github.com/WildbookOrg/detecttools
  • https://github.com/WildbookOrg/pyrf
    docs: http://bluemellophone.github.io/pyrf

The IBEIS module itself:

  • https://github.com/WildbookOrg/ibeis

IBEIS Development Environment Setup

NOTE: this section is outdated.

# The following install script install ibeis and all dependencies. # If it doesnt you can look at the older instructions which follow # and try to figure it out. After running this you should have a code # directory with all of the above repos.

# Navigate to your code directory

export

CODE_DIR

=

~/code mkdir

$CODE_DIR

cd

$CODE_DIR

# Clone IBEIS

git clone https://github.com/Erotemic/ibeis.git

cd

ibeis

# Install the requirements for super_setup

pip install -r requirements/super_setup.txt

# Install the development requirements (note-these are now all on pypi, so # this is not strictly necessary)

python super_setup.py ensure

# NOTE: you can use super_setup to do several things

python super_setup.py --help python super_setup.py versions python super_setup.py status python super_setup.py check python super_setup.py pull

# Run the run_developer_setup.sh file in each development repo

python super_setup.py develop

# Or you can also just do to use pypi versions of dev repos:

python setup.py develop

# Optional: set a workdir and download a test dataset

.python -m ibeis.dev .python -m ibeis.dev -t mtest python -m ibeis.dev -t nauts ./reset_dbs.py python -m ibeis --set-workdir ~/data/work --preload-exit python -m ibeis -e ensure_mtest

# make sure everyhing is set up correctly

python -m ibeis --db PZ_MTEST

Running Tests

The new way of running tests is with xdoctest, or using the “run_doctests.sh” script.

Example usage

(Note: This list is far from complete)

#-------------------- # Main Commands #--------------------

python -m ibeis.main <optional-arguments>

[

--help

] python -m ibeis.dev <optional-arguments>

[

--help

]

# main is the standard entry point to the program # dev is a more advanced developer entry point

# ** NEW 7-23-2015 **: the following commands are now equivalent and do not # have to be specified from the ibeis source dir if ibeis is installed

python -m ibeis <optional-arguments>

[

--help

] python -m ibeis.dev <optional-arguments>

[

--help

]

# Useful flags. # Read code comments in dev.py for more info. # Careful some commands don't work. Most do. # --cmd # shows ipython prompt with useful variables populated # -w, --wait # waits (useful for showing plots) # --gui # starts the gui as well (dev.py does not show gui by default, main does) # --web # runs the program as a web server # --quiet # turns off most prints # --verbose # turns on verbosity # --very-verbose # turns on extra verbosity # --debug2 # runs extra checks # --debug-print # shows where print statments occur # -t [test]

#-------------------- # PSA: Workdirs: #-------------------- # IBEIS uses the idea of a work directory for databases. # Use --set-workdir <path> to set your own, or a gui will popup and ask you about it

./main.py --set-workdir /raid/work --preload-exit ./main.py --set-logdir /raid/logs/ibeis --preload-exit python -m ibeis.dev --set-workdir ~/data/work --preload-exit

# use --db to specify a database in your WorkDir # --setdb makes that directory your default directory

python -m ibeis.dev --db <dbname> --setdb

# Or just use the absolute path

python -m ibeis.dev --dbdir <full-dbpath>

#-------------------- # Examples: # Here are are some example commands #-------------------- # Run the queries for each roi with groundtruth in the PZ_MTEST database # using the best known configuration of parameters

python -m ibeis.dev --db PZ_MTEST --allgt -t best python -m ibeis.dev --db PZ_MTEST --allgt -t score

# View work dir

python -m ibeis.dev --vwd --prequit

# List known databases

python -m ibeis.dev -t list_dbs

# Dump/Print contents of params.args as a dict

python -m ibeis.dev --prequit --dump-argv

# Dump Current SQL Schema to stdout

python -m ibeis.dev --dump-schema --postquit

#------------------ # Convert a hotspotter database to IBEIS #------------------

# NEW: You can simply open a hotspotter database and it will be converted to IBEIS

python -m ibeis convert_hsdb_to_ibeis --dbdir <path_to_hsdb>

# This script will exlicitly conver the hsdb

python -m ibeis convert_hsdb_to_ibeis --hsdir <path_to_hsdb> --dbdir <path_to_newdb>

#--------- # Ingest examples #--------- # Ingest raw images

python -m ibeis.dbio.ingest_database --db JAG_Kieryn

#--------- # Run Tests #---------

./run_tests.py

#---------------- # Test Commands #---------------- # Set a default DB First

python -m ibeis.dev --setdb --dbdir /path/to/your/DBDIR python -m ibeis.dev --setdb --db YOURDB python -m ibeis.dev --setdb --db PZ_MTEST python -m ibeis.dev --setdb --db PZ_FlankHack

# List all available tests

python -m ibeis.dev -t

help

# Minimal Database Statistics

python -m ibeis.dev --allgt -t info

# Richer Database statistics

python -m ibeis.dev --allgt -t dbinfo

# Print algorithm configurations

python -m ibeis.dev -t printcfg

# Print database tables

python -m ibeis.dev -t tables

# Print only the image table

python -m ibeis.dev -t imgtbl

# View data directory in explorer/finder/nautilus

python -m ibeis.dev -t vdd

# List all IBEIS databases

python -m ibeis list_dbs

# Delete cache

python -m ibeis delete_cache --db testdb1

# Show a single annotations

python -m ibeis.viz.viz_chip show_chip --db PZ_MTEST --aid

1

--show

# Show annotations 1, 3, 5, and 11

python -m ibeis.viz.viz_chip show_many_chips --db PZ_MTEST --aids

=

1

,3,5,11 --show

# Database Stats for all our important datasets:

python -m ibeis.dev --allgt -t dbinfo --db PZ_MTEST

|

grep -F

"[dbinfo]"

# Some mass editing of metadata

python -m ibeis.dev --db PZ_FlankHack --edit-notes python -m ibeis.dev --db GZ_Siva --edit-notes python -m ibeis.dev --db GIR_Tanya --edit-notes python -m ibeis.dev --allgt -t dbinfo --db GZ_ALL --set-all-species zebra_grevys

# Current Experiments:

# Main experiments

python -m ibeis --tf draw_annot_scoresep --db PZ_MTEST -a default -t best --show python -m ibeis.dev -e draw_rank_cdf --db PZ_MTEST --show -a timectrl

# Show disagreement cases

ibeis --tf draw_match_cases --db PZ_MTEST -a default:size

=

20

\

-t default:K

=[

1

,4

]

\

--filt :disagree

=

True,index

=

:4 --show

# SMK TESTS

python -m ibeis.dev -t smk2 --allgt --db PZ_MTEST --nocache-big --nocache-query --qindex

:20 python -m ibeis.dev -t smk2 --allgt --db PZ_MTEST --qindex

20

:30 --va

# Feature Tuning

python -m ibeis.dev -t test_feats -w --show --db PZ_MTEST --allgt --qindex

1

:2 python -m ibeis.dev -t featparams -w --show --db PZ_MTEST --allgt python -m ibeis.dev -t featparams_big -w --show --db PZ_MTEST --allgt

# NEW DATABASE TEST

python -m ibeis.dev -t best --db seals2 --allgt

# Testing Distinctivness Parameters

python -m ibeis.algo.hots.distinctiveness_normalizer --test-get_distinctiveness --show --db GZ_ALL --aid

2

python -m ibeis.algo.hots.distinctiveness_normalizer --test-get_distinctiveness --show --db PZ_MTEST --aid

10

python -m ibeis.algo.hots.distinctiveness_normalizer --test-test_single_annot_distinctiveness_params --show --db GZ_ALL --aid

2

# 2D Gaussian Curves

python -m vtool_ibeis.patch --test-test_show_gaussian_patches2 --show

# Test Keypoint Coverage

python -m vtool_ibeis.coverage_kpts --test-gridsearch_kpts_coverage_mask --show python -m vtool_ibeis.coverage_kpts --test-make_kpts_coverage_mask --show

# Test Grid Coverage

python -m vtool_ibeis.coverage_grid --test-gridsearch_coverage_grid_mask --show python -m vtool_ibeis.coverage_grid --test-sparse_grid_coverage --show python -m vtool_ibeis.coverage_grid --test-gridsearch_coverage_grid --show

# Test Spatially Constrained Scoring

python -m ibeis.algo.hots.vsone_pipeline --test-compute_query_constrained_matches --show python -m ibeis.algo.hots.vsone_pipeline --test-gridsearch_constrained_matches --show

# Test VsMany ReRanking

python -m ibeis.algo.hots.vsone_pipeline --test-vsone_reranking --show python -m ibeis.algo.hots.vsone_pipeline --test-vsone_reranking --show --homog

# Problem cases with the back spot

python -m ibeis.algo.hots.vsone_pipeline --test-vsone_reranking --show --homog --db GZ_ALL --qaid

425

python -m ibeis.algo.hots.vsone_pipeline --test-vsone_reranking --show --homog --db GZ_ALL --qaid

662

python -m ibeis.dev -t custom:score_method

=

csum,prescore_method

=

csum --db GZ_ALL --show --va -w --qaid

425

--noqcache

# Shows vsone results with some of the competing cases

python -m ibeis.algo.hots.vsone_pipeline --test-vsone_reranking --show --homog --db GZ_ALL --qaid

662

--daid_list

=

425

,342,678,233

# More rerank vsone tests

python -c

"import utool as ut; ut.write_modscript_alias('Tbig.sh', 'dev.py', '--allgt --db PZ_Master0')"

sh Tbig.sh -t custom:rrvsone_on

=

True custom sh Tbig.sh -t custom:rrvsone_on

=

True custom --noqcache

#---- # Turning back on name scoring and feature scoring and restricting to rerank a subset # This gives results that are closer to what we should actually expect

python -m ibeis.dev --allgt -t custom

\

custom:rrvsone_on

=

True,prior_coeff

=

1

.0,unconstrained_coeff

=

.0,fs_lnbnn_min

=

,fs_lnbnn_max

=

1

\

custom:rrvsone_on

=

True,prior_coeff

=

.5,unconstrained_coeff

=

.5,fs_lnbnn_min

=

,fs_lnbnn_max

=

1

\

custom:rrvsone_on

=

True,prior_coeff

=

.1,unconstrained_coeff

=

.9,fs_lnbnn_min

=

,fs_lnbnn_max

=

1

\

--print-bestcfg

#----

#---- # VsOneRerank Tuning: Tune linar combination

python -m ibeis.dev --allgt -t

\

custom:fg_weight

=

.0

\ \

custom:rrvsone_on

=

True,prior_coeff

=

1

.0,unconstrained_coeff

=

.0,fs_lnbnn_min

=

.0,fs_lnbnn_max

=

1

.0,nAnnotPerNameVsOne

=

200

,nNameShortlistVsone

=

200

\ \

custom:rrvsone_on

=

True,prior_coeff

=

.5,unconstrained_coeff

=

.5,fs_lnbnn_min

=

.0,fs_lnbnn_max

=

1

.0,nAnnotPerNameVsOne

=

200

,nNameShortlistVsone

=

200

\ \

--db PZ_MTEST

#--print-confusion-stats --print-gtscore #----

# Testing no affine invaraiance and rotation invariance

python -m ibeis.dev -t custom:AI

=

True,RI

=

True custom:AI

=

False,RI

=

True custom:AI

=

True,RI

=

False custom:AI

=

False,RI

=

False --db PZ_MTEST --show

Caveats / Things we are not currently doing

  • We do not add or remove points from kdtrees. They are always rebuilt


Carrying Squishy to Rank 1 in Rocket League Season 3


Follow Me:
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GarrettG NRG SquishyMuffinz RocketLeague SuperSonicLegend

นอกจากการดูบทความนี้แล้ว คุณยังสามารถดูข้อมูลที่เป็นประโยชน์อื่นๆ อีกมากมายที่เราให้ไว้ที่นี่: ดูเพิ่มเติม

Carrying Squishy to Rank 1 in Rocket League Season 3

V GAMING vs Z9 GAMING | VGM vs Z9 – VÒNG BẢNG AWC 2021 | BẢNG B NGÀY 21/6


V GAMING vs Z9 GAMING | VGM vs Z9 VÒNG BẢNG AWC 2021 | BẢNG B NGÀY 21/6
AWC2021 FightTogetherRiseTogether
•••••••••••••••••••••••••••••••
Garena Liên Quân Mobile \u0026 Liên Quân Mobile eSports Garena là 2 kênh YouTube chính thức của hội đồng Liên Quân, đồng thời là đơn vị duy nhất giữ bản quyền sản xuất toàn bộ nội dung game và giải đấu Liên Quân Mobile (Arena of Valor).

V GAMING vs Z9 GAMING | VGM vs Z9 - VÒNG BẢNG AWC 2021 | BẢNG B NGÀY 21/6

MAD vs DEW | Z9 vs CIV | BRU vs DEW | CIV vs VGM | FL vs BRU | VGM vs ONE -Vòng bảng AWC 2021 -19.06


MAD vs DEW | Z9 vs CIV | BRU vs DEW | CIV vs VGM | FL vs BRU | VGM vs ONE Vòng bảng AWC 2021 19.06
32:02 Khai mạc AWC 2021
52:51 MAD vs DEW ván 1
1:21:37 MAD vs DEW ván 2
2:12:51 Z9 vs CIV ván 1
2:50:18 Z9 vs CIV ván 2
3:39:34 BRU vs DEW ván 1
4:12:26 BRU vs DEW ván 2
4:57:12 CIV vs VGM ván 1
5:23:36 CIV vs VGM ván 2
6:07:23 FL vs BRU ván 1
6:43:22 FL vs BRU ván 2
7:46:42 VGM vs ONE ván 1
8:21:07 VGM vs ONE ván 2
AWC2021 FightTogetherRiseTogether
•••••••••••••••••••••••••••••••
Garena Liên Quân Mobile và Học chơi Liên Quân là 2 kênh YouTube chính thức của hội đồng Liên Quân, đồng thời là đơn vị duy nhất giữ bản quyền sản xuất toàn bộ nội dung game và giải đấu Liên Quân Mobile (Arena of Valor).

MAD vs DEW | Z9 vs CIV | BRU vs DEW | CIV vs VGM | FL vs BRU | VGM vs ONE -Vòng bảng AWC 2021 -19.06

FNF VS One Night at Flumpty


DawnTaurus: https://youtube.com/DawnTaurus
Membership: https://www.youtube.com/DarkTaurus/join
V.S One Night at Flumpty ft. Blam, Gruckfuss, Beaver, Owl, Flumpty
https://gamebanana.com/mods/339648
ONAF 3 original game
https://gamejolt.com/games/onaf3/529078
Friday Night Funkin’
https://ninjamuffin24.itch.io/funkin
My Other Activities
https://facebook.com/DarkTaurusYT
https://instagram.com/DarkTaurusYT
https://twitter.com/DarkTaurusYT
https://shop.spreadshirt.com/DarkTaurus
Timestamps:
0:00 Birthday Boy Blam
2:14 Grunkfuss
3:30 Beaver \u0026 Owl
4:42 Beaver Mummy
5:40 Flumpty
ONAF FNF

FNF VS One Night at Flumpty

Ván Đấu Quyết Định Giúp Team Flash Bước Chân Vào Chung Kết Tổng Và Lần Nữa Tái Đấu Với SGP


Ván Đấu Quyết Định Giúp Team Flash Bước Chân Vào Chung Kết Tổng Và Lần Nữa Tái Đấu Với SGP
► Đón xem livestream của mình tại page vào lúc 20h23h tối
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ADCGAMINGadcteamflash
Bản quyền thuộc về ADC GAMING
Do not reup
Editor Gin

Ván Đấu Quyết Định Giúp Team Flash Bước Chân Vào Chung Kết Tổng Và Lần Nữa Tái Đấu Với SGP

นอกจากการดูบทความนี้แล้ว คุณยังสามารถดูข้อมูลที่เป็นประโยชน์อื่นๆ อีกมากมายที่เราให้ไว้ที่นี่: ดูบทความเพิ่มเติมในหมวดหมู่Wiki

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