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The dataset comprises 90 different animal images. Initially, l structured for one-vs-rest classification, followed by binary classification and then a 5-class classification problem. l evaluated each model's performance using classification matrices.

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Moses-Mk/ML-Image-Classification

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ML-Image-Classification

The dataset comprises 90 different animal images. Initially, l structured for one-vs-rest classification, followed by binary classification and then a 5-class classification problem. l evaluated each model's performance using classification matrices.

  1. Dataset Preparation:

    • Organize the dataset for one-vs-rest classification. Perform binary classification using existing architectures and then restructure for 5-class classification. Use 3-fold cross-validation to assess the model.
  2. Model Development:

    • Build a custom CNN model without using existing architectures like ResNet or DenseNet.
  3. Training and Evaluation:

    • Train the model on prepared datasets for one-vs-rest and 5-class classification.
    • Generate classification matrices for visualization.
  4. Convolutional Layer Visualization:

    • Plot the output of all convolutional layers and discuss the insights on automatically created features.

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The dataset comprises 90 different animal images. Initially, l structured for one-vs-rest classification, followed by binary classification and then a 5-class classification problem. l evaluated each model's performance using classification matrices.

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