Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Flow Forecast | 1,759 | 3 months ago | 101 | gpl-3.0 | Python | |||||
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting). | ||||||||||
Ailia Models | 1,708 | 3 months ago | 297 | Python | ||||||
The collection of pre-trained, state-of-the-art AI models for ailia SDK | ||||||||||
Pygod | 1,138 | 3 months ago | 7 | July 19, 2023 | 3 | bsd-2-clause | Python | |||
A Python Library for Graph Outlier Detection (Anomaly Detection) | ||||||||||
Training_extensions | 1,119 | 1 | a month ago | 55 | October 31, 2023 | 54 | apache-2.0 | Python | ||
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™ | ||||||||||
Getting Things Done With Pytorch | 873 | 3 years ago | 13 | apache-2.0 | Jupyter Notebook | |||||
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT. | ||||||||||
Ganomaly | 767 | a year ago | 44 | mit | Python | |||||
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training | ||||||||||
Deep Svdd Pytorch | 538 | a year ago | 17 | mit | Python | |||||
A PyTorch implementation of the Deep SVDD anomaly detection method | ||||||||||
Outlier Exposure | 378 | 3 years ago | apache-2.0 | Python | ||||||
Deep Anomaly Detection with Outlier Exposure (ICLR 2019) | ||||||||||
Deeplog | 320 | 9 months ago | 22 | mit | Python | |||||
Pytorch Implementation of DeepLog. | ||||||||||
Deepadots | 270 | 4 years ago | 7 | mit | Python | |||||
Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series". |