基于Keras-Surgeon的面罩检测分类器和模型修剪

Alok Negi, Prachi Chauhan, Krishan Kumar, R. S. Rajput
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引用次数: 29

摘要

在冠状病毒新世界采取多学科行动,限制了大流行的传播。有趣的是,人工智能小组是这些努力的一部分。这种基于结果的方法用于帮助扫描、评估、预测和跟踪当前患者和可能的潜在患者。追踪社交距离或识别口罩的进展尤其成为头条新闻。目前大多数先进的人脸识别方法都是基于深度学习的,而深度学习依赖于大量的人脸样本。在冠状病毒爆发期间,为了有效避免COVID-19病毒的传播,几乎每个人都戴着口罩。我们的目标是训练一个定制的深度学习模型,帮助检测一个人是否戴着面具,并使用Keras-Surgeon研究模型修剪的概念。模型剪枝可以有效地减小模型尺寸,从而可以在嵌入式系统上容易地实现和推断。
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Face Mask Detection Classifier and Model Pruning with Keras-Surgeon
Multidisciplinary initiatives in the new world of coronavirus were combined to limit the spread of the pandemic. Interestingly, the AI group was a part of those efforts. This result-based approach is used to help scan, assess, predict and track current patients and possibly potential patients. Developments for tracking social distances or recognizing face masks have made headlines in particular. Most current advanced approaches to face mask recognition are built based on deep learning which is dependent on a large number of face samples. Nearly everybody wears a mask during corona virus outbreak in order to effectively avoid the spread of COVID-19 virus. Our goal is to train a customized deep learning model that helps to detect even if or not a person wears a mask and study the concept of model pruning with Keras-Surgeon. Model pruning can be efficient in reducing model size, so that it can be easily implemented and inferred on embedded systems.
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