Transparent Deep Learning: Comparing Scratch to SOTA for Land Cover Classification

Understanding deep learning requires building it from scratch. This study compares six implementations on the EuroSAT satellite dataset (27,000 images, 10 classes): fully connected DNNs and CNNs built from scratch using NumPy/CuPy, TensorFlow baselines, and EfficientNet-B0 transfer learning. Scratch implementations achieve 49.11% (DNN) and 82.30% (CNN) test accuracy, while library-based models reach 94.00% (TF CNN) and 97.63% (EfficientNetB0)—a 48.52 percentage point improvement. Key debugging insights include: gradient vanishing diagnosed through per-layer monitoring, Adam momentum overshoot, 100× speedups from vectorization, and batch normalization providing 15.3 point accuracy gains. The 6-block CNN failure reveals dimensional bottleneck issues absent from standard curricula. This work provides unprecedented transparency into implementation challenges—gradient flow dynamics, numerical stability requirements, and why production libraries outperform educational code.