Performance Analysis of Original Implementation of ResNet50-Mask-RCNN using Transfer Learning: A Benchmark Data for Backbone-Improved Based Future Comparative Studies

In computer vision applications such as object recognition and instance segmentation, deep learning techniques—more specifically, the Mask-RCNN architecture based on ResNet50—have shown exceptional capabilities. This work makes use of transfer learning to provide a thorough analysis of the ResNet50-Mask-RCNN model’s first implementation to understand its performance features and constraints on a range of datasets and situations. Additionally, the study presents benchmark data that will be used as a standard reference for the next comparative assessments that will concentrate on techniques with enhanced backbones. With significant convergence in validation loss values during training, validation results demonstrate the model’s efficacy in object identification and classification tasks. As shown by the Precision-Recall curve data, the model also achieves an impressive average mAP of 0.8095 and shows resilience with high recall rates at various precision levels. The AUC-ROC value of 0.9399 highlights how well the model can distinguish between positive and negative instances. High detection rates for the target class were noted in the testing results, which support the model’s effectiveness.