https://github.com/sardhendu/cifar10-object-recognition

{Python}: Mix-Match of several image Processing and Machine Learning Techniques for object recognition
https://github.com/sardhendu/cifar10-object-recognition

classification computer-vision deep-learning deep-neural-networks machine-learning-algorithms python3 tensorflow

Last synced: about 1 year ago
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{Python}: Mix-Match of several image Processing and Machine Learning Techniques for object recognition

• Host: GitHub
• URL: https://github.com/sardhendu/cifar10-object-recognition
• Owner: Sardhendu
• Created: 2017-01-28T08:30:12.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2018-03-24T00:25:48.000Z (over 8 years ago)
• Last Synced: 2025-04-12T23:45:02.386Z (about 1 year ago)
• Topics: classification, computer-vision, deep-learning, deep-neural-networks, machine-learning-algorithms, python3, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 148 MB
• Stars: 10
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# CIFAR10

CIFAR10- Object Recognition

The repository contains implementation and evaluation of several Models for CIFAR10 Object recognition

Below are some Feature Extraction and Modules implemented.

    1. Feature Extraction: 

            --> RGB 

            --> Standarized Image

            --> Edge Features.

            --> Histogram of oriented Gradients

            --> ZCA whitened

    

    2. Models:

            --> K-nearest Neighbors

            --> Logistic Regression

            --> Support Vector Machines

            --> Deep Neural Networks

            --> Convolutional Neural Networks

            

    3. Evaluation:

            --> Model Accuracy

            --> Confusion Matrix

Note: The majority of the code resides inside the MODEL folder. For Simplicity and deep understanding of several techniques/model, we emlploy and evaluate the models for only 2 classes (Airplane and Cat). However the code can be easily be extented for all the 10 labels, which would require a little bit of hyperparameter tuning.

Paper/Code References:

    1. http://cs231n.github.io/convolutional-networks/

    2. ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

    3. Maxout Networks : Ian J. Goodfellow David Warde-Farley Mehdi Mirza, Aaron Courville Yoshua Bengio

    4. Dropout: A Simple Way to Prevent Neural Networks from Overfitting - Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov

    5. Going deeper with convolutions - Christian Szegedy, Pierre Sermanet, Wei Liu , Yangqing Jia , Dumitru Erhan , Scott Reed , Dragomir Anguelov , Vincent Vanhoucke , Andrew Rabinovich