A naukri.com initiative
Dev
2w
143
Image Credit: Dev
Image Classification with Convolutional Neural Networks (CNNs)
- Using CNNs to classify handwritten digits from the MNIST dataset, starting with a simple multilayer perceptron before transitioning to CNNs.
- MNIST dataset, a benchmark in computer vision, consists of 70,000 labeled images of handwritten digits 0-9.
- Data normalization involved rescaling pixel values from [0, 255] to [0, 1] for better neural network performance.
- Reshaping images into (28, 28, 1) format for CNN input with grayscale images represented as a single channel.
- Defining a CNN model in PyTorch with convolutional layers, max pooling, fully connected layers, ReLU activations, and dropout.
- Training the CNN model over multiple epochs, observing decreasing loss and increasing accuracy over training data.
- Achieving a test accuracy of 99.04% on the unseen test set after training the CNN model.
- Visualizing test images along with model predictions for digit recognition.
- Plotting the evolution of training loss and accuracy over the training epochs, showcasing model improvement.
- The project demonstrates the effectiveness of CNNs in image classification tasks, with implications for broader computer vision applications.
Read Full Article
8 Likes
For uninterrupted reading, download the app