Explain CNN Architecture for Image Classification

Your question is Explain CNN Architecture for Image Classification. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

Business Context

ShopVision, an e-commerce catalog platform, wants a junior ML engineer to explain and prototype a convolutional neural network (CNN) for classifying product images into catalog categories. The goal is not just to define CNN layers, but to show how the architecture maps to a real image classification workflow and how you would evaluate it in practice.

Dataset

You are given a labeled image dataset of product photos collected from the mobile seller app.

Feature Group	Count	Examples
Image pixels	150,000 images	RGB images resized to 128x128
Metadata (optional, not required)	3	upload_device, aspect_ratio, brightness_score
Labels	12 classes	shoes, bags, watches, shirts, electronics

Size: 150K images, 12 target classes
Target: Multiclass product category
Class balance: Moderately imbalanced; largest class is 18%, smallest is 4%
Missing data: ~2% corrupted or unreadable images; some images have inconsistent lighting/backgrounds

Success Criteria

A good solution should:

Achieve top-1 accuracy >= 82% on a held-out test set
Clearly explain the role of convolution, activation, pooling, and fully connected layers
Show a training pipeline that can run on a single GPU within a reasonable time budget
Include overfitting controls and a clear evaluation approach

Constraints

Inference latency should stay under 30 ms/image in batch serving
The model should be simple enough for an interview explanation
Training budget is limited to a single mid-range GPU
The team prefers an architecture that is easy to debug and extend

Deliverables

Explain the core CNN architecture and why each layer is used.
Build a baseline CNN for multiclass image classification.
Describe preprocessing and augmentation choices.
Evaluate the model with appropriate classification metrics.
Discuss tradeoffs between a simple custom CNN and transfer learning.