CNN Image Classifier - 4-Block Architecture
About This Architecture
Four-block convolutional neural network designed for 48x48 grayscale image classification with progressive filter expansion from 64 to 512 channels. Each block pairs Conv2D layers with BatchNorm for stable training, followed by MaxPooling for spatial reduction and Dropout for regularization. The architecture flows through GlobalAvgPooling into a classifier head with Dense layers, culminating in a 7-class Softmax output. This pattern demonstrates best practices for preventing overfitting while maintaining discriminative capacity across hierarchical feature levels. Fork this diagram to customize filter counts, adjust dropout rates, or adapt the input dimensions for your specific classification task.
People also ask
What is a good CNN architecture for 48x48 image classification with regularization?
This 4-block CNN progressively expands filters from 64 to 512 channels, using BatchNorm after each Conv2D layer for training stability, MaxPooling for spatial reduction, and Dropout (0.2–0.4) to prevent overfitting. The classifier head applies GlobalAvgPooling, a Dense(256) layer, and Softmax output for 7-class classification.
- Domain:
- Ml Pipeline
- Audience:
- Machine learning engineers building image classification models with deep learning frameworks
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