Most popular metrics used to evaluate object detection algorithms.

mean Average Precision - This code evaluates the performance of your neural net for object recognition.

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.

A package to read and convert object detection datasets (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.

Function to calculate mAP for set of detected boxes and annotated boxes.

PyTorch-Based Evaluation Tool for Co-Saliency Detection

This repository contains the official implementation of the NeurIPS'21 paper, ROADMAP: Robust and Decomposable Average Precision for Image Retrieval.

A Python evaluation metrics package for surgical action triplet recognition

ML/CNN Evaluation Metrics Package

A simple script to calculate the mAP using PascalVOC2012 and COCO standards for object detection

Understanding of use of mAP as a metric for Objects Detection problems

PR曲線を用いた平均適合率の計算の方法についての説明と、実装による理解の確認を行います。

Python library for Object Detection metrics.

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.

YOLOV8 - Object detection

Built various machine learning models for banks to develop effective credit rating

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.

My first machine learning project.

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.

Evaluation for object detection models