Image Classifier CNN Architecture
This neural network diagram shows the main stages of a compact convolutional image classifier. A prepared RGB product image enters two convolution stages, with pooling between them to reduce the feature map. Global average pooling turns the learned features into a small representation that a softmax classifier can use to choose among the product categories. The diagram is useful in a model proposal, a technical presentation, or a handoff between a data team and an application team. It deliberately shows the architecture at layer level rather than every kernel, activation, and tensor shape. Add those details when the drawing is for implementation review.
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Scenario
Classifying catalog images
Key decisions
- Input size: Keep image preprocessing consistent with training.
- Feature extraction: Use two convolution stages before classification.
- Pooling: Reduce spatial detail before the final classifier.
When to reuse this
Use this high-level architecture when explaining a compact image classifier, not when documenting every tensor operation.
Frequently asked questions
What does a convolutional layer do?
Why use pooling?
What is a softmax output?
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