This repository contains an implementation of the AlexNet model for image classification tasks. The project utilizes the CIFAR-10 dataset, consisting of 10 classes of small images, resized to fit the input size required by AlexNet.
AlexNet is a convolutional neural network (CNN) originally designed for large-scale image classification tasks. It uses multiple convolutional layers, pooling layers, and fully connected layers to extract and classify image features. In this repository:
- AlexNet has been adapted to handle the CIFAR-10 dataset.
- Includes data preprocessing, training, and evaluation pipelines.
- Features learning rate scheduling and early stopping for optimal training.
This project uses the CIFAR-10 dataset, which contains:
- 20,000 training images and 4,000 test images (subset of CIFAR-10).
- 10 classes: Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck.
Using 20,000 training samples and 4,000 test samples, the model achieved the following results:
- Precision: 70.97%
- Recall: 70.63%
- F1-Score: 70.66%
AlexNet is a convolutional neural network (CNN) originally introduced in 2012 by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton.
For more details, you can read the official paper: "ImageNet Classification with Deep Convolutional Neural Networks".