TransferLearning

Transfer Learning isn’t magic — but when your dataset is small and time is short, it’s often the most rational weapon.

In my Cats vs Dogs experiment, I compared: 1️⃣ CNN from scratch — flexible but needs more data and tuning 2️⃣ MobileNetV2 Transfer Learning — faster convergence and more stable on small data

💡 Lessons Learned: Start with light augmentations (flip, small rotations, mild zoom). Too much = underfitting. Fine-tune only the last ~40 layers for a good balance between domain adaptation and overfitting risk. Use conservative learning rates + EarlyStopping to save time and keep validation accuracy stable. Every choice has a trade-off: fast & stable (TL) vs flexible & exploratory (baseline). All experiments are reproducible — outputs include best models, training history, and plots.

Curious: what’s your go-to strategy when balancing baseline CNNs and transfer learning? herewith : github : https://lnkd.in/gAFfv-yN

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