Artificial Intelligence-Based Quality Control of Pharmaceutical Tablets: A Visual Dataset and Benchmark for Automated...

Background/Objectives: Automated visual inspection of pharmaceutical tablets is increasingly important for detecting manufacturing defects and maintaining consistent product quality. Progress in artificial intelligence-based defect classification is, however, constrained by the scarcity of publicly accessible image datasets covering defective tablets under diverse visual conditions. This study introduces the PharmaVision (PV) dataset, a public visual dataset and benchmark for classifying normal and defective compressed tablets. Methods: The PV dataset contains 640 smartphone-acquired images spanning varied tablet products, backgrounds, and lighting, with fixed tablet-level splits (190/190 training, 30/30 validation, and 100/100 test images for the normal and defective classes, respectively). Twelve representative architectures spanning classical convolutional networks, mobile networks, and vision transformers were benchmarked under an identical protocol using accuracy, precision, recall, and harmonic mean of precision and recall (F1-score), with 95% confidence intervals and paired statistical tests. Cross-dataset generalization was assessed on the independent pillQC dataset without fine-tuning, and efficiency was profiled using parameters, Giga floating-point operations (GFLOPs), latency, and throughput. Results: EfficientNetV2-S achieved the highest F1-score on the PV test set (88.56%), although paired statistical tests did not distinguish it from Xception (87.08%) or Swin-B (85.71%). On pillQC, GoogLeNet and EfficientNetV2-S showed the strongest cross-dataset performance, whereas both vision transformers declined relative to their in-domain results. AlexNet gave the highest theoretical throughput (602.41 frames per second (FPS), median latency 1.66 ms), whereas ShuffleNetV2 and MobileNetV3-small offered the smallest parameter counts. Conclusions: In-domain accuracy, cross-dataset robustness, and measured latency diverged, with no single architecture best on all three axes. The PV dataset, code, and evaluation protocol are publicly available on GitHub to support reproducible AI-based tablet quality control.