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

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

Information Retrieval with Vector Space Model for News Article

Python library for Object Detection metrics.

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

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

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).

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

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

Evaluate a detection model performance

Description of computing object tracking metrics.

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

A flow to compile YOLOv3/SSD using TVM and run the compiled model on CPU to calculate mAP

Evaluation for object detection models

Implementing the training pipeline for YOLOv4 using PyTorch

Improving the performance of the information retrieval system by normalization of data .Elasticsearch engine was used and bm25 model was used to compare the performance of the IR system.