Deep Neural Network for Image Recognition Based on the Caffe Framework

Komar, Myroslav; Yakobchuk, Pavlo; Golovko, Vladimir; Dorosh, Vitaliy; Sachenko, Anatoliy

doi:10.1109/dsmp.2018.8478621

Cited by 38 publications

(21 citation statements)

References 17 publications

(20 reference statements)

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“…The dataset contains 5,000 portraits of 525 individuals wearing masks and 90,000 pictures of the same 525 subjects with no masks. The whole project was implemented in Python using Deep Learning Libraries like PyTorch [12], Caffe [13], and Computer Vision libraries like OpenCV.…”

Section: Methodsmentioning

confidence: 99%

“…The decisions taken at this stage of growth are vital to the success of the network. Transformation and Normalization [14] are two commonly used methods of pre-processing. Transformation requires the change of raw data inputs to create a new input to the network, while normalization is a transformation performed on new data input to disperse the data equally and scale it to an appropriate range for the network.…”

Section: Pre-processingmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Face Mask Detector with PyTorch and Deep Learning

Basha¹,

Pravallika²,

Shankar³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The outbreak ofacoronavirus disease in 2019 (COVID-19) has created a global health epidemic that has had a major effect on the way we view our environment and our daily lives. The Covid-19 affected numbers are rising at a tremendous pace. Because of that, many countries face an economic catastrophe, recession, and much more. One thing we should do is to separate ourselves from society, remain at home, and detach ourselves from the outside world. But that's no longer a choice, people need to earn to survive, and nobody can remain indefinitely within their homes. As a precaution, people should wear masks while keeping social distance, but some ignore such things and walk around.OBJECTIVES: To develop aFace Mask Detector with OpenCV, PyTorch, and Deep Learning that helps to detect whether or not a person wears a mask.METHODS: A Neural Network model called ResNet is trained on the dataset. Furthermore, this work makes use of the inbuilt Face Detector after training. Finally, we predict whether or not a person is wearing a mask along with the percentage of the face covered or uncovered.RESULTS: The validation results have been proposed to be 97% accurate when compared to applying different algorithms.CONCLUSION: This Face Mask Detection System was found to be apt for detecting whether or not people wear masks in public places which contribute to their health and also to the health of their contacts in this COVID-19 pandemic.

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Section: Methodsmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

An Efficient Face Mask Detector with PyTorch and Deep Learning

Basha¹,

Pravallika²,

Shankar³

2021

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

show abstract

“…Initially, we chose a DL framework called Caffe [21] (convolutional architecture for fast feature embedding), which has modularity and fast speed, for the experiment. Caffe was first developed by Yangqing Jia and, currently, hundreds of developers are involved in development of this open-source framework in github.…”

Section: Experimental Environment and Scenariosmentioning

confidence: 99%

Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture

2020

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Colossal amounts of unstructured multimedia data are generated in the modern Internet of Things (IoT) environment. Nowadays, deep learning (DL) techniques are utilized to extract useful information from the data that are generated constantly. Nevertheless, integrating DL methods with IoT devices is a challenging issue due to their restricted computational capacity. Although cloud computing solves this issue, it has some problems such as service delay and network congestion. Hence, fog computing has emerged as a breakthrough way to solve the problems of using cloud computing. In this article, we propose a strategy that assigns a portion of the DL layers to fog nodes in a fog-computing-based smart agriculture environment. The proposed deep learning entrusted to fog nodes (DLEFN) algorithm decides the optimal layers of DL model to execute on each fog node, considering their available computing capacity and bandwidth. The DLEFN individually calculates the optimal layers for each fog node with dissimilar computational capacities and bandwidth. In a similar experimental environment, comparison results clearly showed that proposed method accommodated more DL application than other existing assignment methods and utilized resources efficiently while reducing network congestion and processing burden on the cloud.

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“…The data set is compiled by using python code to read the data file format with the extension file name in xml format and input it into the neural network layer of the custom DNNs model. The DNNs model is the basic prototype of the neural network model is made by installing Caffe Deep Learning Framework [27], and the weight value of the neural network of each layer of Fast R-CNN on DNNs is adjusted by python code program compilation.…”

Section: Network Trainingmentioning

confidence: 99%

AE-RTISNet: Aeronautics Engine Radiographic Testing Inspection System Net with Improved Fast Region-based Convolutional Neural Networks Framework

Chen¹,

Juang²

2020

Preprint

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To ensure the safety in aircraft flying, we aim use of the deep learning methods of nondestructive examination with multiple defect detection paradigms for X-ray image detection posed. The use of the Fast Region-based Convolutional Neural Networks (Fast R-CNN) driven model seeks to augment and improve existing automated Non-Destructive Testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers insufficient types of X-ray aeronautics engine defect data samples can thus pose another problem in training model tackling multiple detections perform accuracy. To overcome this issue, we employ a deep learning paradigm of transfer learning tackling both single and multiple detection. Overall the achieve result get more then 90% accuracy based on the AE-RTISNet retrained with 8 types of defect detection. Caffe structure software to make networks tracking detection over multiples Fast R-CNN. We consider the AE-RTISNet provide best results to the more traditional multiple Fast R-CNN approaches simpler translate to C++ code and installed in the Jetson™ TX2 embedded computer. With the use of LMDB format, all images using input images of size 640 × 480 pixel. The results scope achieves 0.9 mean average precision (mAP) on 8 types of material defect classifiers problem and requires approximately 100 microseconds.

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Deep Neural Network for Image Recognition Based on the Caffe Framework

Cited by 38 publications

References 17 publications

An Efficient Face Mask Detector with PyTorch and Deep Learning

An Efficient Face Mask Detector with PyTorch and Deep Learning

Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture

AE-RTISNet: Aeronautics Engine Radiographic Testing Inspection System Net with Improved Fast Region-based Convolutional Neural Networks Framework

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