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Face Mask Detection Classifier and Model Pruning with Keras-Surgeon

机译：面罩检测分类器与Keras-Surgeon的模型修剪

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摘要

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.
机译：冠状病毒新世界的多学科倡议被组合以限制大流行的蔓延。有趣的是，AI集团是这些努力的一部分。基于结果的方法用于帮助扫描，评估，预测和跟踪当前患者以及可能的潜在患者。追踪社会距离或识别面罩的发展已经尤其成为了头条新闻。基于深度学习的深度学习，大多数对面膜识别的最新方法是基于依赖于大​​量的面部样本。几乎每个人都在电晕病毒爆发期间佩戴面膜，以有效地避免Covid-19病毒的传播。我们的目标是培训定制的深度学习模式，帮助检测一个人佩戴面具并研究模型修剪的概念，并用Keras-Surgeon。模型修剪可以在降低模型尺寸方面有效，从而可以在嵌入式系统上轻松实现和推断它。

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