Python TIoU-metric Resources

This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch. The code was prepared to the final version of the accepted manuscript in AISTATS and is provided as-is.

MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition Paper: MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition accepted fo

Distance-Ratio-Based Formulation for Metric Learning Environment Python3 Pytorch (http://pytorch.org/) (version 1.6.0+cu101) json tqdm Preparing datas

Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime Created by Prarthana Bhattacharyya. Disclaimer: This is n

Cross-modal Retrieval using Transformer Encoder Reasoning Networks This project reimplements the idea from "Transformer Reasoning Network for Image-Te

Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples (WACV 2022) and Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning (TPAMI 2022 - in submission)

SeMantic and linguistic UndeRstanding Fusion (SMURF) Automatic caption evaluation metric described in the paper "SMURF: SeMantic and linguistic UndeRs

Easily Extendable Basic Deep Metric Learning Pipeline Karsten Roth ([email protected]), Biagio Brattoli ([email protected]) When using thi

News December 27: v1.1.0 New loss functions: CentroidTripletLoss and VICRegLoss Mean reciprocal rank + per-class accuracies See the release notes Than

DSAMNet The pytorch implementation for "A Deeply-supervised Attention Metric-based Network and an Open Aerial Image Dataset for Remote Sensing Change

[AAAI 2022] Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding Official Pytorch implementation of Negative Sample Matter

2D-TAN (Optimized) Introduction This is an optimized re-implementation repository for AAAI'2020 paper: Learning 2D Temporal Localization Networks for

Clustering Clustering Application in Python Using scikit-learn This repository contains the prediction of baseball metric clusters using MLB Statcast

About The Project This is the old code for bitcoin risk metric, the whole purpose form it is to help you DCA your investment according to bitcoin risk

Open-ReID Open-ReID is a lightweight library of person re-identification for research purpose. It aims to provide a uniform interface for different da

QAConv Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting This PyTorch code is proposed in

lpips-pytorch Description Developing perceptual distance metrics is a major topic in recent image processing problems. LPIPS[1] is a state-of-the-art

TIoU-metric Release on 27/03/2019. This repository is built on the ICDAR 2015 evaluation code. If you propose a better metric and require further eval

FENSE The metric, Fluency ENhanced Sentence-bert Evaluation (FENSE), for audio caption evaluation, proposed in the paper "Can Audio Captions Be Evalua

DeepFMPO v3D Code accompanying the paper "On the value of using 3D-shape and electrostatic similarities in deep generative methods". The paper can be

Near-Duplicate Video Retrieval with Deep Metric Learning This repository contains the Tensorflow implementation of the paper Near-Duplicate Video Retr

Cross-View Kernel Similarity Metric Learning Using Pairwise Constraints for Person Re-identification T M Feroz Ali, Subhasis Chaudhuri, ICVGIP-20-21

Proxy Anchor Loss for Deep Metric Learning Unofficial pytorch, tensorflow and mxnet implementations of Proxy Anchor Loss for Deep Metric Learning. Not

A simple script written using symbolic python that takes as input a desired metric and automatically calculates and outputs the Christoffel Pseudo-Tensor, Riemann Curvature Tensor, Ricci Tensor, Scalar Curvature and the Kretschmann Scalar

Density-aware Chamfer Distance This repository contains the official PyTorch implementation of our paper: Density-aware Chamfer Distance as a Comprehe

Density-aware Chamfer Distance This repository contains the official PyTorch implementation of our paper: Density-aware Chamfer Distance as a Comprehe

PyEMD: Fast EMD for Python PyEMD is a Python wrapper for Ofir Pele and Michael Werman's implementation of the Earth Mover's Distance that allows it to

Automatic Video Captioning Evaluation Metric --- EMScore Overview For an illustration, EMScore can be computed as: Installation modify the encode_text

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

Deep Metric Learning Implementation of several methods for deep metric learning in Chainer v4.2.0. Proxy-NCA: No Fuss Distance Metric Learning using P

Non-Metric Space Library (NMSLIB) Important Notes NMSLIB is generic but fast, see the results of ANN benchmarks. A standalone implementation of our fa

A general purpose recommender metrics library for fair evaluation.

Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases [Papers 1, 2][Project page] [Video] The implementation of the papers Temporal

LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, with a validation scheme of choice, based on the chosen metric.

Audio Quality Platform - AQP An Open Modular Python Platform for Objective Speech and Audio Quality Metrics AQP is a highly modular pipeline designed

This repository contains the Matlab implementations for the following multi-target filtering/tracking algorithms: - Folder PMBM contains the implemen

EgonNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale Paper: EgoNN: Egocentric Neural Network for Point Cloud

PyTorch implementation of MSBG hearing loss model and MBSTOI intelligibility metric This repository contains the implementation of MSBG hearing loss m

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Asymmetric metric learning This is the official code that enables the reproduction of the results from our paper: Asymmetric metric learning for knowl

About This repository the official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning. The config files contain the s

BLEURT: a Transfer Learning-Based Metric for Natural Language Generation BLEURT is an evaluation metric for Natural Language Generation. It takes a pa

Face Recognition in PyTorch By Alexey Gruzdev and Vladislav Sovrasov Introduction A repository for different experimental Face Recognition models such

TensorFlow Similarity is a python package focused on making similarity learning quick and easy.

Manifold Matching via Deep Metric Learning for Generative Modeling A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generat

GeDML is an easy-to-use generalized deep metric learning library

Deep Relational Metric Learning This repository is the official PyTorch implementation of Deep Relational Metric Learning. Framework Datasets CUB-200-

PrimitiveNet Source code for the paper: Jingwei Huang, Yanfeng Zhang, Mingwei Sun. [PrimitiveNet: Primitive Instance Segmentation with Local Primitive

Map Metrics for Trajectory Quality Map metrics toolkit provides a set of metrics to quantitatively evaluate trajectory quality via estimating consiste

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks.

DIML Created by Wenliang Zhao*, Yongming Rao*, Ziyi Wang, Jiwen Lu, Jie Zhou This repository contains PyTorch implementation for paper Towards Interpr

UMIC This repository provides an unferenced image captioning metric from our ACL 2021 paper UMIC: An Unreferenced Metric for Image Captioning via Cont

This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of object detection architectures and models pretrained on the COCO dataset, are passed to a Deep Sort algorithm which tracks the objects. It can track any object that your Yolov5 model was trained to detect.

Embedding Transfer with Label Relaxation for Improved Metric Learning Official PyTorch implementation of CVPR 2021 paper Embedding Transfer with Label

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning Official PyTorch implementation of "Proxy Synthesis: Learning with Synthetic

VANET Code reproduce for paper "Vehicle Re-identification with Viewpoint-aware Metric Learning" Introduction This is the implementation of article VAN

Auto-Seg-Loss By Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, Jifeng Dai This is the official implementation of the ICLR 2021 paper Auto

Intro This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales Vehicle Sam

News March 3: v0.9.97 has various bug fixes and improvements: Bug fixes for NTXentLoss Efficiency improvement for AccuracyCalculator, by using torch i

UNION Automatic Evaluation Metric described in the paper UNION: An UNreferenced MetrIc for Evaluating Open-eNded Story Generation (EMNLP 2020). Please

TedEval: A Fair Evaluation Metric for Scene Text Detectors Official Python 3 implementation of TedEval | paper | slides Chae Young Lee, Youngmin Baek,

Tsinghua Dogs classification with Deep Metric Learning 1. Introduction Tsinghua Dogs dataset Tsinghua Dogs is a fine-grained classification dataset fo

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

Accelerated deep learning R&D PyTorch framework for Deep Learning research and development. It focuses on reproducibility, rapid experimentation, and

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met