Popular GitHub Repositories for Exponential Moving Average

mean Average Precision - This code evaluates the performance of your neural net for object recognition.

AI Search & RAG Without Moving Your Data. Get instant answers from your company's knowledge across 100+ apps while keeping data secure. Deploy in minutes, not months.

Find big moving stocks before they move using machine learning and anomaly detection

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.

Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

Official implementation of the paper: MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (CVPR 2021)

ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST

Compare SELUs (scaled exponential linear units) with other activations on MNIST, CIFAR10, etc.

ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild. ECCV 2022.

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

An Unsupervised Learning Framework for Moving Object Detection From Videos

Hierarchical Time Series Forecasting with a familiar API

A package to read and convert object detection datasets (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.

Implementation of Mega, the Single-head Attention with Multi-headed EMA architecture that currently holds SOTA on Long Range Arena

HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems

[ICRA2024] RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud

[CVPR 2021] DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

Implementation of ETSformer, state of the art time-series Transformer, in Pytorch

Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried out in (x, y, \theta) configuration space, i.e. agents' orientation are taken into account.