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Experimental Design

Five experiments with a total of 408 inference runs across all devices.

Experiment 1 — Core Comparison

  • Fixed: batch=1, imgsz=640, task=segment
  • Varies: format (PyTorch FP32, TensorRT FP16, TensorRT INT8), approach (scratch, pretrained), architecture, model size
  • RTX 5090: train + infer (PyTorch), export + infer (TensorRT FP16/INT8) | Jetsons: export + infer (TensorRT)

Experiment 2 — Input Size Impact

  • Fixed: batch=1, format=PyTorch FP32, task=segment
  • Varies: imgsz (320, 1280), approach (scratch, pretrained), architecture, model size
  • All devices: inference only (reuses weights from Experiment 1)

Experiment 3 — Batch Throughput

  • Fixed: imgsz=640, format=PyTorch FP32, approach=scratch, task=segment
  • Varies: batch (4, 8, 16), architecture, model size
  • All devices: inference only (reuses weights from Experiment 1)

Experiment 4 — Detection vs Segmentation

  • Fixed: batch=1, imgsz=640, format=PyTorch FP32
  • Varies: approach (scratch, pretrained), architecture, model size
  • RTX 5090: train + infer | Jetsons: inference only

Experiment 5 — Class Imbalance Impact

  • Fixed: batch=1, imgsz=640, format=PyTorch FP32, task=segment
  • Varies: approach (scratch_balanced, pretrained_balanced), architecture, model size (nano, small, medium, large)
  • RTX 5090: train + infer | Jetsons: inference only
  • Compares per-class mAP against unbalanced baselines from Experiment 1

Weighted sampling

Images containing rare classes (IV-5, IV-6, IV-3) are sampled more frequently during training via a WeightedRandomSampler. The validation set is unchanged, ensuring mAP scores reflect true model performance.

Run Distribution

Device Training Export Inference Total
RTX 5090 48 32 136 216
Jetson Orin AGX 0 32 136 168
Jetson Orin Nano 0 32 136 168
Total 48 96 408 552

Weight reuse

Experiments 2 and 3 reuse trained weights from Experiment 1. Inference-only runs do not require retraining.