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Performed feature engineering, cross-validation (5 fold) on baseline and cost-sensitive (accounting for class imbalance) Decision trees and Logistic Regression models and compared performance. Used appropriate performance metrics i.e., AUC ROC, Average Precision and Balanced Accuracy. Outperformed baseline model.
This is a novel average precision calculation named hybrid N-point interpolation method to eliminate the average precision distortion in KITTI 3D Object Detection Benchmark.
In this Power Bi project, all the data has been compared using different shifts and the delayed time. Here the range of the upper bound is also analyzed where the maximum upper bound is predicted in the morning shift compared to mid-day and evening shift. The delay time in morning (A.M.) and Evening (P.M.) ranges have also been predicted.
A package to read and convert object detection datasets (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.
Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.