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image-classification

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    mlp
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    Image classification

    This project is an image classification program. It is trained and tested using the CIFAR dataset. Two methods are used to perform image classification: k-nearest neighbours and neural networks.

    Description

    First, the CIFAR dataset is loaded and prepared to be used. Then, the k-nearest neighbours method is used to perform image classification. Finally, neural networks are used to perform image classification.

    Prepare the CIFAR dataset

    Each image is made 32x32 pixels. Each pixel is in color, and therefore has 3 numbers representing it. For each batch we have the following parameters:

    • batch_size = 10000,
    • data_size = 32x32x3 = 3072. For the entire datasets (5 train batches and 1 test batch), we have the following parameters:
    • batch_size = 60000,
    • data_size = 32x32x3 = 3072. Each batch is unpickled. All batches are concatenated into a matrix data and a list labels. They are then suffled and split to create training and test datasets.

    k-nearest neighbours

    Features

    Background

    Installation

    Requirements

    Usage

    Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.

    Author

    Sophie BESSAC

    License

    For open source projects, say how it is licensed.