Analysis of hyperparameter optimization effects on lightweight deep models for real-time image classification.

Lightweight convolutional and transformer-based networks are increasingly used for real-time image classification on resource-constrained hardware, yet their practical performance is highly sensitive to training hyperparameters. This work systematically quantifies how controlled hyperparameter choices affect both accuracy and deployability for seven modern lightweight backbones-ConvNeXt-Tiny, EfficientNetV2-S, MobileNetV3-L, MobileViT v2 (S/XS), RepVGG-A2, and TinyViT-21M trained from scratch on a class-balanced 90K/10K subset of ImageNet-1K under a standardized 300-epoch protocol. We isolate the effects of learning-rate magnitude and cosine scheduling, optimizer selection (SGD vs. AdamW where appropriate), and progressively stronger regularization via RandAugment, Mixup, CutMix, and label smoothing, complemented by constrained automated searches (Optuna and population-based training). Beyond training-time analysis, we add a deployment-focused evaluation: inference latency and throughput are benchmarked on an NVIDIA L40s GPU across batch sizes 1-512, and edge feasibility is examined via Edge CPU Platform under sustained workloads. Results show that hyperparameter tuning without architectural modification yields consistent accuracy gains ([Formula: see text] Top-1 over baseline) and reveals architecture-dependent stability regions. Several models deliver strong real-time operating points: MobileNetV3-L and RepVGG-A2 achieve very low latency with high throughput on GPU, while edge tests highlight the limited benefit of batching on low-power CPUs and the importance of latency-centric model choice. The code and logs may be seen at:  https://github.com/VineetKumarRakesh/lcnn-opt .