What is a CNN?
Srujan P -
Welcome to my third blog post on Deep CNNs for Image Classification
In this post, I’ll be diving into the fundamentals of Convolutional Neural Networks (CNNs) and the first steps in building a deep learning-based image classifier.
Why CNNs?
Convolutional Neural Networks (CNNs) are a special type of artificial neural network designed to process and analyze visual data. Unlike traditional neural networks, which treat images as a flat array of numbers, CNNs preserve spatial relationships between pixels. This ability makes them incredibly powerful for tasks like image classification, object detection, and facial recognition. CNNs automatically learn important features without requiring manual engineering, making them highly effective in image recognition tasks.
What Have I Been Doing?
Recently, I’ve been exploring the basics of deep learning, particularly CNNs, to build an image classifier from scratch. So far, I have built sample CNNs and CNNs that work on different image classification, such as fruits and numbers. This journey has involved reading research papers, experimenting with datasets, and understanding how different CNN architectures impact performance. These datasets actually harbor much of the difficulty in this project as finding a large dataset with fine art counterfeits is not only niche, it is almost counterintuitive. While some of the underlying math and theory can be complex, the good news is that implementing CNNs has become much more accessible thanks to modern deep-learning frameworks. Thankfully, due to my mathematics coursework, much of the math and theory of CNNs are quite easy to understand.
Let’s do a basic overview of CNNs:
How Do CNNs Work?
CNNs consist of multiple layers, each serving a specific purpose:
- Convolutional Layers: Scan the image using filters (also known as kernels) that detect patterns like edges, textures, and shapes.
- Pooling Layers: Reduce the size of the image representation, making the model more efficient and less prone to overfitting.
- Fully Connected Layers: Classify the image based on the detected patterns.
Getting Started: First Steps
- Choosing a Dataset: As mentioned earlier, many of the available datasets are fantastic, but they lack specificity for my use case. I will opt for using multiple datasets like WikiArt, ArtEmis, and SemART.
- Preprocess the Data: This step involves resizing images, normalizing pixel values, and splitting data into training and test sets. Essentially, this is organizing or tidying up my data, so it is easier for the model to work with it.
- Build a Simple CNN Model: Using a framework like TensorFlow or PyTorch, I can define a basic CNN with a few convolutional and pooling layers.
- Train the Model: Feed data into the model and optimize it using techniques like backpropagation and gradient descent.
- Evaluate and Improve: Test the model on unseen data, tweak hyperparameters, and experiment with deeper architectures to improve accuracy
That concludes my blog post for this week. I appreciate you reading it!
Thanks,
Srujan