Balancing accuracy and efficiency in particle tracking: analyzing image resolution and batch size trade-offs

Abstract

Reliable pore-scale particle tracking is pivotal for understanding non-Fickian transport in dual-porosity media, yet the computational burden of deep-learning pipelines can limit their practical use. This study systematically quantifies how two fundamental hyper-parameters—image resolution and training batch size—jointly shape accuracy and efficiency in a state-of-the-art tracker that couples SimVP video prediction with Trackpy-derived ground truth. Fluorescence microscopy movies (1328 frames, 3672 × 4100 px) of colloid migration through a PDMS micromodel were down-scaled to 64–400 px and trained with batch sizes of 4 or 8. Point-wise errors (MSE, MAE, RMSE), structural fidelity (SSIM, PSNR), and perceptual quality (LPIPS) were evaluated on validated trajectories, 1000 unseen pairs, and a blind hold-out set. Increasing resolution from 64 to 300 px raises pixel-based errors (MSE × ≈700) and inference time (0.26 s → 5 s batch⁻1) but unlocks a 20-fold rise in detected particles, while perceptual metrics remain above high-quality thresholds (SSIM > 0.999). Reducing the batch from 8 to 4 consistently halves to tenfold improves all error metrics, boosts PSNR by ~ 8 dB, and lowers LPIPS by up to 85%, with minimal variance penalties. Recommended operating points are: batch 4 @ 300 px for maximum fidelity, batch 4 @ 400 px for the densest particle fields, and batch 8 @ 128–256 px where real-time throughput dominates. These results provide concrete guidelines for balancing accuracy, particle yield, and computational cost in next-generation pore-scale imaging studies.