This project demonstrates image classification using Convolutional Neural Networks (CNNs) and the Keras deep learning library. A CNN model has been applied to the popular Fashion MNIST dataset to recognize 10 different clothing types from images.
Project Objective:
- Build and train a CNN model on the Fashion MNIST dataset to accurately classify clothing images.
- Gain practical experience with image classification using Keras and CNNs.
Dataset:
Fashion MNIST: Dataset of grayscale images categorized into 10 classes of clothing (t-shirt, dress, etc.). Contains 60,000 training images and 10,000 testing images.
Model Architecture:
- Convolutional layers are used to extract features from images.
- Pooling layers are used to reduce spatial dimensions.
- Dense layers are used to perform classifications.
- ReLU activation function is used in intermediate layers for non-linear learning and efficient gradient propagation.
- Softmax activation function is applied to the output layer to produce a probability distribution indicating the likelihood of each class for a given image.
Results:
Achieved 0.8673 accuracy on the test set. This might be improved further with hyperparameter tuning.