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Kernel Methods for Machine Learning

MoSIG DSAI, MSIAM 2021/22 Grenoble INP - Ensimag

Introduction

This repository contains the code for the data challenge of the Kernel Methods for Machine Learning course. The task is image classification with kernel methods.

Authors

  • Gabriele Degola

Public score: 0.616 Private score: 0.602 Leaderboard position: 3

Data

Images are drawn from the CIFAR-10 dataset and organized as csv files: each row contains an image of 32 x 32 pixels.

Run

The following command reproduces the best submission. Predictions are stored in Yte.csv.

python start.py --xtr data/Xtr.csv --ytr data/Ytr.csv --xte data/Xte.csv

Structure

Code is organized in the following files:

  • start.py is the main script;
  • models.py contains our implementation of kernel ridge regression;
  • kernels.py contains implemented kernels;
  • utils.py contains functions and classes for data processing.

Dependencies

Code relies on the following Python libraries:

  • numpy, scipy for basic operations;
  • pandas for data loading and storing;
  • tqdm for showing progresses;
  • scikit-image for computation of histograms of oriented gradients;
  • scikit-learn for label binarization only, required for multi-class classification.

About

Image classification with kernel methods

Topics

classificationensimaghog-featuresmachine-learningpython

Resources

Readme
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GitHub - gabridego/kernel-methods: Image classification with kernel methods · GitHub
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Kernel Methods for Machine Learning
MoSIG DSAI, MSIAM 2021/22
Grenoble INP - Ensimag
Introduction
This repository contains the code for the data challenge of the Kernel Methods for Machine Learning course. The task is image classification with kernel methods.
Authors
  • Gabriele Degola
Public score: 0.616
Private score: 0.602
Leaderboard position: 3
Data
Images are drawn from the CIFAR-10 dataset and organized as csv files: each row contains an image of 32 x 32 pixels.
Run
The following command reproduces the best submission. Predictions are stored in Yte.csv.
python start.py --xtr data/Xtr.csv --ytr data/Ytr.csv --xte data/Xte.csv
Structure
Code is organized in the following files:
  • start.py is the main script;
  • models.py contains our implementation of kernel ridge regression;
  • kernels.py contains implemented kernels;
  • utils.py contains functions and classes for data processing.
Dependencies
Code relies on the following Python libraries:
  • numpy, scipy for basic operations;
  • pandas for data loading and storing;
  • tqdm for showing progresses;
  • scikit-image for computation of histograms of oriented gradients;
  • scikit-learn for label binarization only, required for multi-class classification.
About
Image classification with kernel methods
Topics
classificationensimaghog-featuresmachine-learningpython
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GitHub - gabridego/kernel-methods: Image classification with kernel methods · GitHub
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Kernel Methods for Machine Learning
MoSIG DSAI, MSIAM 2021/22
Grenoble INP - Ensimag
Introduction
This repository contains the code for the data challenge of the Kernel Methods for Machine Learning course. The task is image classification with kernel methods.
Authors
  • Gabriele Degola
Public score: 0.616
Private score: 0.602
Leaderboard position: 3
Data
Images are drawn from the CIFAR-10 dataset and organized as csv files: each row contains an image of 32 x 32 pixels.
Run
The following command reproduces the best submission. Predictions are stored in Yte.csv.
python start.py --xtr data/Xtr.csv --ytr data/Ytr.csv --xte data/Xte.csv
Structure
Code is organized in the following files:
  • start.py is the main script;
  • models.py contains our implementation of kernel ridge regression;
  • kernels.py contains implemented kernels;
  • utils.py contains functions and classes for data processing.
Dependencies
Code relies on the following Python libraries:
  • numpy, scipy for basic operations;
  • pandas for data loading and storing;
  • tqdm for showing progresses;
  • scikit-image for computation of histograms of oriented gradients;
  • scikit-learn for label binarization only, required for multi-class classification.
About
Image classification with kernel methods
Topics
classificationensimaghog-featuresmachine-learningpython
Resources
Readme
Activity
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Watchers
1 watching
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0 forks
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - gabridego/kernel-methods: Image classification with kernel methods · GitHub
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Kernel Methods for Machine Learning
MoSIG DSAI, MSIAM 2021/22
Grenoble INP - Ensimag
Introduction
This repository contains the code for the data challenge of the Kernel Methods for Machine Learning course. The task is image classification with kernel methods.
Authors
  • Gabriele Degola
Public score: 0.616
Private score: 0.602
Leaderboard position: 3
Data
Images are drawn from the CIFAR-10 dataset and organized as csv files: each row contains an image of 32 x 32 pixels.
Run
The following command reproduces the best submission. Predictions are stored in Yte.csv.
python start.py --xtr data/Xtr.csv --ytr data/Ytr.csv --xte data/Xte.csv
Structure
Code is organized in the following files:
  • start.py is the main script;
  • models.py contains our implementation of kernel ridge regression;
  • kernels.py contains implemented kernels;
  • utils.py contains functions and classes for data processing.
Dependencies
Code relies on the following Python libraries:
  • numpy, scipy for basic operations;
  • pandas for data loading and storing;
  • tqdm for showing progresses;
  • scikit-image for computation of histograms of oriented gradients;
  • scikit-learn for label binarization only, required for multi-class classification.
About
Image classification with kernel methods
Topics
classificationensimaghog-featuresmachine-learningpython
Resources
Readme
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, 'i');
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - gabridego/kernel-methods: Image classification with kernel methods · GitHub
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Kernel Methods for Machine Learning
MoSIG DSAI, MSIAM 2021/22
Grenoble INP - Ensimag
Introduction
This repository contains the code for the data challenge of the Kernel Methods for Machine Learning course. The task is image classification with kernel methods.
Authors
  • Gabriele Degola
Public score: 0.616
Private score: 0.602
Leaderboard position: 3
Data
Images are drawn from the CIFAR-10 dataset and organized as csv files: each row contains an image of 32 x 32 pixels.
Run
The following command reproduces the best submission. Predictions are stored in Yte.csv.
python start.py --xtr data/Xtr.csv --ytr data/Ytr.csv --xte data/Xte.csv
Structure
Code is organized in the following files:
  • start.py is the main script;
  • models.py contains our implementation of kernel ridge regression;
  • kernels.py contains implemented kernels;
  • utils.py contains functions and classes for data processing.
Dependencies
Code relies on the following Python libraries:
  • numpy, scipy for basic operations;
  • pandas for data loading and storing;
  • tqdm for showing progresses;
  • scikit-image for computation of histograms of oriented gradients;
  • scikit-learn for label binarization only, required for multi-class classification.
About
Image classification with kernel methods
Topics
classificationensimaghog-featuresmachine-learningpython
Resources
Readme
Activity
Stars
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1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - gabridego/kernel-methods: Image classification with kernel methods · GitHub
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Kernel Methods for Machine Learning
MoSIG DSAI, MSIAM 2021/22
Grenoble INP - Ensimag
Introduction
This repository contains the code for the data challenge of the Kernel Methods for Machine Learning course. The task is image classification with kernel methods.
Authors
  • Gabriele Degola
Public score: 0.616
Private score: 0.602
Leaderboard position: 3
Data
Images are drawn from the CIFAR-10 dataset and organized as csv files: each row contains an image of 32 x 32 pixels.
Run
The following command reproduces the best submission. Predictions are stored in Yte.csv.
python start.py --xtr data/Xtr.csv --ytr data/Ytr.csv --xte data/Xte.csv
Structure
Code is organized in the following files:
  • start.py is the main script;
  • models.py contains our implementation of kernel ridge regression;
  • kernels.py contains implemented kernels;
  • utils.py contains functions and classes for data processing.
Dependencies
Code relies on the following Python libraries:
  • numpy, scipy for basic operations;
  • pandas for data loading and storing;
  • tqdm for showing progresses;
  • scikit-image for computation of histograms of oriented gradients;
  • scikit-learn for label binarization only, required for multi-class classification.
About
Image classification with kernel methods
Topics
classificationensimaghog-featuresmachine-learningpython
Resources
Readme
Activity
Stars
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1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - gabridego/kernel-methods: Image classification with kernel methods · GitHub
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Kernel Methods for Machine Learning
MoSIG DSAI, MSIAM 2021/22
Grenoble INP - Ensimag
Introduction
This repository contains the code for the data challenge of the Kernel Methods for Machine Learning course. The task is image classification with kernel methods.
Authors
  • Gabriele Degola
Public score: 0.616
Private score: 0.602
Leaderboard position: 3
Data
Images are drawn from the CIFAR-10 dataset and organized as csv files: each row contains an image of 32 x 32 pixels.
Run
The following command reproduces the best submission. Predictions are stored in Yte.csv.
python start.py --xtr data/Xtr.csv --ytr data/Ytr.csv --xte data/Xte.csv
Structure
Code is organized in the following files:
  • start.py is the main script;
  • models.py contains our implementation of kernel ridge regression;
  • kernels.py contains implemented kernels;
  • utils.py contains functions and classes for data processing.
Dependencies
Code relies on the following Python libraries:
  • numpy, scipy for basic operations;
  • pandas for data loading and storing;
  • tqdm for showing progresses;
  • scikit-image for computation of histograms of oriented gradients;
  • scikit-learn for label binarization only, required for multi-class classification.
About
Image classification with kernel methods
Topics
classificationensimaghog-featuresmachine-learningpython
Resources
Readme
Activity
Stars
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Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
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GitHub - gabridego/kernel-methods: Image classification with kernel methods · GitHub
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Kernel Methods for Machine Learning
MoSIG DSAI, MSIAM 2021/22
Grenoble INP - Ensimag
Introduction
This repository contains the code for the data challenge of the Kernel Methods for Machine Learning course. The task is image classification with kernel methods.
Authors
  • Gabriele Degola
Public score: 0.616
Private score: 0.602
Leaderboard position: 3
Data
Images are drawn from the CIFAR-10 dataset and organized as csv files: each row contains an image of 32 x 32 pixels.
Run
The following command reproduces the best submission. Predictions are stored in Yte.csv.
python start.py --xtr data/Xtr.csv --ytr data/Ytr.csv --xte data/Xte.csv
Structure
Code is organized in the following files:
  • start.py is the main script;
  • models.py contains our implementation of kernel ridge regression;
  • kernels.py contains implemented kernels;
  • utils.py contains functions and classes for data processing.
Dependencies
Code relies on the following Python libraries:
  • numpy, scipy for basic operations;
  • pandas for data loading and storing;
  • tqdm for showing progresses;
  • scikit-image for computation of histograms of oriented gradients;
  • scikit-learn for label binarization only, required for multi-class classification.
About
Image classification with kernel methods
Topics
classificationensimaghog-featuresmachine-learningpython
Resources
Readme
Activity
Stars
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1 watching
Forks
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