High-fidelity performance metrics for generative models in PyTorch

Precisely align, move, and measure+match objects and mesh parts in your 3D scenes.

A general purpose recommender metrics library for fair evaluation.

Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.

Multi-class metrics for Tensorflow

Training with FP16 weights in PyTorch

Object Detection Evaluation Tools

Python implementation of ROUGE

Automatic method for the recognition of hand gestures for the categorization of vowels and numbers in Colombian sign language based on Neural Networks (Perceptrons), Support Vector Machine and K-Nearest Neighbor for classifier /// Método automático para el reconocimiento de gestos de mano para la categorización de vocales y números en lenguaje d…

Script to compute Precision, Recall, AvP and MAP and to plot PR curves in the context of Information Retrieval evaluation.

Better multi-class confusion matrix plots for Scikit-Learn, incorporating per-class and overall evaluation measures.

This is a web based elective course recommender system implemented with flask and Sklearn

An Assessment on Unmanned Aerial System (UAS) Photogrammetry without Ground Control

Tuning of parameters of ML algorithms to optimise precision/f-score for fault detection in softwares

jsondiff is a json diff utility. It can compare two JSON files, using strings, prefixes, or regex to filter required/optional fields, and apply relative or absolute precision tolerance per each numeric field or globally; prints the diff between 2 json files. It can optionally accept a config with required or optional fields.

Precision-recall-gain for Python

This code build up a predicting model use the Machine learning algorithms such as Decision Tree, k-Nearest Neighbors etc. on the Vehicle to predict the departure action

Square root any precision

一种面向运维的PLUG IN式异常探测框架

Evaluation and agreement scripts for the DISCOSUMO project. Each evaluation script takes both manual annotations as automatic summarization output. The formatting of these files is highly project-specific. However, the evaluation functions for precision, recall, ROUGE, Jaccard, Cohen's kappa and Fleiss' kappa may be applicable to other domains too.