https://github.com/shalabymhd/sift-fv-imagenet

The final project for the Applied Machine Learning (COMP 551) course at McGill University, under the supervision of Sarath Chandar.
https://github.com/shalabymhd/sift-fv-imagenet

Last synced: 22 days ago
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The final project for the Applied Machine Learning (COMP 551) course at McGill University, under the supervision of Sarath Chandar.

• Host: GitHub
• URL: https://github.com/shalabymhd/sift-fv-imagenet
• Owner: shalabymhd
• Created: 2018-12-26T05:37:01.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-12-30T23:15:05.000Z (about 6 years ago)
• Last Synced: 2024-10-30T04:39:36.520Z (2 months ago)
• Language: Python
• Homepage:
• Size: 660 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# SIFT-FV-imagenet

The final project for the Applied Machine Learning (COMP 551) course at McGill University, under the supervision of Sarath Chandar.

# Scripts

  ## Downloading Data

    1) /ImageNet_Downloads/sysnets.txt: contains the references for all the classes to be downloaded

    2) /ImageNet_Downloads/DataDownload.py: downloads class by class based on the sysnets provided in the text file; runs parallel to the representation learning algorithm. 

  

  ## Representation Learning

    1) /scripts/representationLearning_SIFT_FV.m: extracts the SIFT descriptors from the images and encodes them into fisher vectors

    2) /others/RepresentationLearning_Histograms.m: a less accurate attempt using the histogram method

    3) /others/RepresentationLearning_VLAD.m: a less accurate attempt using Vector of Locally Aggregated Descriptors (VLAD)

 

  ## Classifiers

    1) /scripts/train_fisher.py: training various different classifiers on the fisher vectors

    2) /scripts/LogisticRegressionTraining.py: hyperparameter tuning on Logistic Regression 

    3) /scripts/SGDTraining.py: hyperparameter tuning on classifier using SGD

    

  ## Others

    1) /scripts/splitter_manual.py: splits the full dataset into training, validation and testing data

    2) /others/splitter.py: first draft of the script to split the data

    3) /others/countlines.py: counts the number of lines in the huge csv files

    4) /others/prepare10Classes.py: extracting only 10 classes

    5) /others/train_fisher_10classes.py: training various different classifiers on the fisher vectors of the 10 classes