Training Wheels for Computers?
Getting Started with CNNs for Image Classification
Convolutional Neural Networks (CNNs) are a powerful tool for analyzing visual data. Unlike traditional neural networks, they preserve spatial relationships between pixels, making them ideal for tasks like image classification and object detection.
Why CNNs?
CNNs automatically learn features like edges, textures, and shapes without manual engineering. This makes them highly effective for recognizing patterns in images.
What is Going on So Far?
I’ve been exploring CNNs by experimenting with datasets and building models for image classification. Finding high-quality datasets for niche applications—like fine art counterfeits—has been challenging. Deep-learning frameworks like TensorFlow and PyTorch make implementation more accessible and possible. I’ve been training CNNs on datasets like WikiArt and ArtEmis to classify fine art images. The challenge lies in finding high-quality data and optimizing architectures for niche use cases.
What are the First Steps in Training CNNs?
- Choose a Dataset – I’m using multiple datasets like WikiArt and ArtEmis.
- Preprocess the Data – Resize, normalize, and split it into training and test sets.
- Build a Basic CNN – Define a simple architecture with convolutional and pooling layers.
- Train the Model – Use backpropagation and gradient descent to optimize performance.
- Evaluate & Improve – Test on unseen data and tweak hyperparameters to boost accuracy.
How to train a CNN?
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Forward Propagation
- Input images pass through convolutional and pooling layers to extract features.
- The output is passed through fully connected layers to produce predictions.
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Cost and Loss Calculation
- A loss function (e.g., cross-entropy for classification) measures prediction error.
- A cost function measures performance or “how wrong the model is.”
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Backpropagation & Gradient Descent
- Gradients of the loss function are computed using backpropagation, a technique that adjusts internal parameters in order to decrease cost or error.
- An optimizer like SGD, Adam, or RMSprop updates model weights to minimize loss.
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Batch Training
- Instead of updating weights after every sample, data is processed in batches for stable updates.
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Validation & Tuning
- After training, the model is evaluated on a test set.
- Hyperparameters (learning rate, batch size, number of layers) are adjusted to improve performance.
I really appreciate you reading this blog post. Hope you learned something about how CNNs are being built and specifically trained.
Thanks,
Srujan
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