There are 3 repositories under mean-average-precision topic.
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.
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
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.
All scripts related to yoloV4 sliding window
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
Mean Average Precision from Scratch using PyTorch
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
Description of computing object tracking metrics.
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
Evaluate a detection model performance
Based on Faster R-CNN, we train model on our mask dataset and leverage data augmentation to preprocess our data. Mean average precision is introduced to evaluate the model performance. You'll see data augmentation and mAP evaluation in detailed explainations, and tutorials of faster-rcnn training
Implementing the training pipeline for YOLOv4 using PyTorch
Segmentation of COVID-19 lession on chest CT images
A flow to compile YOLOv3/SSD using TVM and run the compiled model on CPU to calculate mAP
Evaluation for object detection models
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.