PyAnomaly: A Pytorch-based Toolkit for Video Anomaly Detection

Abstract

Video anomaly detection is an essential task in computer vision which attracts massive attention from academia and industry. The existing approaches are implemented in diverse deep learning frameworks and settings, making it difficult to reproduce the results published by the original authors. Undoubtedly, this phenomenon is detrimental to the development of Video Anomaly detection and community communication. In this paper, we present a PyTorchbased video anomaly detection toolbox, namely PyAnomaly that contains high modular and extensible components, comprehensive and impartial evaluation platforms, a friendly manageable system configuration, and the abundant engineering deployment functions. To make it easy-to-use and easy-to-extend, we implement the architecture by hooks and registers functionality. Remarkably, we have reproduced the comparable experimental results of six representative methods as those published by the original authors, and we will release these pre-trained models with more rich configurations. To our best knowledge, the PyAnomaly is the first open-source tool in video anomaly detection and is available at https://github.com/YuhaoCheng/PyAnomaly CCS CONCEPTS • Software and its engineering → Software libraries and repositories; • Computing methodologies → Computer vision.

Handwritten Character Recognition using Deep Learning Algorithm with Machine Learning Classifier

“…In the proposed method shown in Fig. 5, the first thing to do is do a feature extraction hook [24] on the global average pool, which is the feature value in the model that has been created, so that later, the feature value will be used to train the machine learning classifier model that will be combined. Global Average Layer transforms a (M x M x N) feature map into a (1 x N) feature map, where (M x M) is the size of the image and N is the number of filters [25].…”