graph LR
ClassificationFLOPsCalculator["ClassificationFLOPsCalculator"]
DetectionFLOPsCalculator["DetectionFLOPsCalculator"]
DetectionResultAnalyzer["DetectionResultAnalyzer"]
SharedFLOPsCore["SharedFLOPsCore"]
CommandLineArgumentParser["CommandLineArgumentParser"]
ResultVisualizerComponent["ResultVisualizerComponent"]
ClassificationFLOPsCalculator -- "Invokes" --> SharedFLOPsCore
ClassificationFLOPsCalculator -- "Receives configuration and input parameters from" --> CommandLineArgumentParser
DetectionFLOPsCalculator -- "Invokes" --> SharedFLOPsCore
DetectionFLOPsCalculator -- "Receives configuration and input parameters from" --> CommandLineArgumentParser
DetectionResultAnalyzer -- "Receives configuration and input parameters from" --> CommandLineArgumentParser
DetectionResultAnalyzer -- "Utilizes" --> ResultVisualizerComponent
This subsystem is designed for the comprehensive evaluation and analysis of deep learning models, encompassing both classification and object detection domains. It features a CommandLineArgumentParser for flexible configuration, feeding parameters to specialized ClassificationFLOPsCalculator and DetectionFLOPsCalculator components that leverage a SharedFLOPsCore for efficient computational cost estimation. For object detection, a dedicated DetectionResultAnalyzer performs quantitative evaluation and collaborates with a ResultVisualizerComponent to provide insightful visual interpretations of model performance. This modular design ensures clear separation of concerns and reusability across different model evaluation tasks.
Computes the computational cost (FLOPs) specifically for classification models. It provides a dedicated interface for resource estimation in the classification domain. This component is crucial for understanding the efficiency of classification models.
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Computes the computational cost (FLOPs) specifically for object detection models. It offers a dedicated interface for resource estimation in the detection domain. Similar to its classification counterpart, this component is vital for assessing detection model efficiency.
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Performs quantitative evaluation (e.g., mAP, accuracy) and manages qualitative visualization of results obtained from detection models. This is the core component for interpreting the performance of detection models.
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Encapsulates the common algorithms and logic for calculating FLOPs, which are utilized by both classification and detection FLOPs calculators. This component promotes code reuse and consistency across different model types, embodying modularity.
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Provides a standardized mechanism for parsing command-line arguments across the different evaluation scripts, enabling flexible configuration and execution. This component is fundamental for external interaction and configurability of the evaluation pipeline.
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Manages the rendering and display of visual outputs for detection results, such as bounding boxes, confidence scores, and ground truth comparisons. This component is essential for qualitative analysis and debugging of detection models.
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