Executive Development Programme in AI Vision Performance Metrics
-- ViewingNowThe Executive Development Programme in AI Vision Performance Metrics is a certificate course designed to provide professionals with a comprehensive understanding of AI vision technologies and performance evaluation. This program is crucial in today's industry, as businesses increasingly rely on AI vision for various applications, from facial recognition to autonomous vehicles.
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โข Introduction to AI Vision Performance Metrics: Understanding the importance of performance metrics in AI vision systems, primary metrics used, and their significance in optimizing system performance. โข Image Quality Assessment (IQA): Measuring subjective and objective image quality, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), visual information fidelity (VIF), and other IQA techniques. โข Object Detection Metrics: Evaluating object detection systems with average precision (AP), intersection over union (IoU), log-average miss rate (mAP), and receiver operating characteristic (ROC) curves. โข Semantic Segmentation Evaluation: Assessing semantic segmentation performance using pixel-wise accuracy, intersection over union (IoU), mean intersection over union (mIoU), and frequency weighted intersection over union (fwIoU). โข Optical Flow Metrics: Evaluating optical flow performance with endpoint error (EPE), average angular error (AAE), and percentage of correct keypoints (PCK). โข Facial Recognition Metrics: Understanding facial recognition performance metrics, including false acceptance rate (FAR), false rejection rate (FRR), genuine acceptance rate (GAR), and equal error rate (EER). โข Performance Metrics for Human Pose Estimation: Evaluating human pose estimation performance using probability of correct keypoint (PCK), percentage of correct parts (PCP), and object keypoint similarity (OKS). โข Ethical and Fairness Considerations in AI Vision Metrics: Analyzing the impact of performance metrics in AI vision systems on ethical considerations and fairness in algorithmic decision making.
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