Most popular metrics used to evaluate object detection algorithms.

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.

Online meter ploter for pytorch. Real time ploting Accuracy, Loss, mAP, AUC, Confusion Matrix

Information Retrieval with Vector Space Model for News Article

A Query-Document pair ranking system using GloVe embeddings and RankCosine.

Python library for Object Detection metrics.

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

Mean Average Precision from Scratch using PyTorch

ArDoCo: Metrics for Classification & Ranking Tasks

All scripts related to yoloV4 sliding window

Evaluates a given detection by calculating the mAP of the bounding box detections results based on a given test set and the detector code. This repo uses for now the yolov3_detector as an example detector for illustration

Using Faster RCNN to detect Scratches/Spots and Dents on damaged cars data

In computer vision, this project meticulously constructs a dataset for precise 'Shoe' tracking using YOLOv8 models. Emphasizing detailed data organization, advanced training, and nuanced evaluation, it provides comprehensive insights. A final project for the Computer Vision cousre on Ottawa Master's in (2023).

Implementing the training pipeline for YOLOv4 using PyTorch

This repository contains a Jupyter notebook. It demonstrates the process of training and evaluating a YOLOv10 model for object detection using the Rock, Paper, Scissors dataset from Roboflow.

Evaluate a detection model performance

Information retrieval system that gives ranked results when a query is given

Information Retrieval with Lucene and CISI dataset. Index documents and search between them with IB, DFR, BM-25, TF-IDF, Boolean, Axiomatic, LM-Dirichlet similarity and calculate Recall, Precision, MAP (Mean Average Precision) and F-Measure

Description of computing object tracking metrics.