Image Classification using LeNet-5 Model.

Overview:

This repository contains image classification on two common datasets (CIFAR-10 and MNIST datasets) using the LeNet-5 architecture.It includes data loading, model definition, training, evaluation metrics, and visualizations.

Key Features

1. Data Loading and Exploration:

   1. CIFAR10 dataset: 60000 32x32 colour images in 10 classes.
   2. MNIST dataset: Handwritten Digit of flower images.

2. Data Visualization:

   • Sample images from CIFAR-10 and MNIST are visualized .

3. Model Architecture:

   • Utilizes the LeNet-5 architecture to accomplish image classification tasks for CIFAR-10 and MNIST.
   • Different input shapes are used for RGB images (CIFAR-10) and grayscale images (MNIST).

4. Evaluation Metrics and Confusion Matrices:

   • confusion matrices are visualized.
   • Precision, recall, and F1 score are calculated for both datasets.

5. Analysis of Model Performance:

   • Insights into the challenges faced by the LeNet-5 model on CIFAR-10 are provided.
   • Factors include data complexity, model architecture, capacity, and potential overfitting.

6. Conclusion:

   • It offers a comprehensive overview of data handling, model architecture, training, and evaluation for CIFAR-10 and MNIST datasets.
   • It concludes with insights into the potential limitations of the LeNet-5 model on CIFAR-10.