Evaluation for object detection models

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

All scripts related to yoloV4 sliding window

Implementing the training pipeline for YOLOv4 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

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.

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.

ArDoCo: Metrics for Classification & Ranking Tasks

Mean Average Precision from Scratch using PyTorch

Python library for Object Detection metrics.

Evaluate a detection model performance

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

Segmentation of COVID-19 lession on chest CT images

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

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

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