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

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

Python library for Object Detection metrics.

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.

Evaluation for object detection models