An Xception Based Convolutional Neural Network for Scene Image Classification with Transfer Learning

2020 ◽  
Author(s):  
Xizhi Wu ◽  
Rongzhe Liu ◽  
Hanqing Yang ◽  
Zizhao Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Scene Image

scholarly journals Convolutional neural network for image classification based on transfer learning technique

2021 ◽  
Author(s):  
Ghassan Mohammed Halawani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Learning Networks ◽  
The Neural Network ◽  
Learning Technique ◽  
Common Architecture

The main purpose of this project is to modify a convolutional neural network for image classification, based on a deep-learning framework. A transfer learning technique is used by the MATLAB interface to Alex-Net to train and modify the parameters in the last two fully connected layers of Alex-Net with a new dataset to perform classifications of thousands of images. First, the general common architecture of most neural networks and their benefits are presented. The mathematical models and the role of each part in the neural network are explained in detail. Second, different neural networks are studied in terms of architecture, application, and the working method to highlight the strengths and weaknesses of each of neural network. The final part conducts a detailed study on one of the most powerful deep-learning networks in image classification – i.e. the convolutional neural network – and how it can be modified to suit different classification tasks by using transfer learning technique in MATLAB.

scholarly journals Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdul Jalil Rozaqi ◽  
Muhammad Rudyanto Arief ◽  
Andi Sunyoto
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Method Development ◽  
Classification Model ◽  
Learning Method ◽  
Potato Leaf ◽  
Leaf Disease

Potatoes are a plant that has many benefits for human life. The potato plant has a problem, namely a disease that attacks the leaves. Disease on potato leaves that is often encountered is early blight and late blight. Image processing is a method that can be used to assist farmers in identifying potato leaf disease by utilizing leaf images. Image processing method development has been done a lot, one of which is by using the Convolutional Neural Network (CNN) algorithm. The CNN method is a good image classification algorithm because its layer architecture can extract leaf image features in depth, however, determining a good CNN architectural model requires a lot of data. CNN architecture will become overfitting if it uses less data, where the classification model has high accuracy on training data but the accuracy becomes poor on test data or new data. This research utilizes the Transfer Learning method to avoid an overfit model when the data used is not ideal or too little. Transfer Learning is a method that uses the CNN architecture that has been trained by other data previously which is then used for image classification on the new data. The purpose of this research was to use the Transfer Learning method on CNN architecture to classify potato leaf images in identifying potato leaf disease. This research compares the Transfer Learning method used to find the best method. The results of the experiments in this research indicate that the Transfer Learning VGG-16 method has the best classification performance results, this method produces the highest accuracy value of 95%.

scholarly journals Pendeteksi Citra Masker Wajah Menggunakan CNN dan Transfer Learning

2021 ◽  
Vol 8 (6) ◽  
pp. 1293
Author(s):  
Mohammad Farid Naufal ◽  
Selvia Ferdiana Kusuma
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Computing Time ◽  
Computation Time

<p class="Abstrak">Pada tahun 2021 pandemi Covid-19 masih menjadi masalah di dunia. Protokol kesehatan diperlukan untuk mencegah penyebaran Covid-19. Penggunaan masker wajah adalah salah satu protokol kesehatan yang umum digunakan. Pengecekan secara manual untuk mendeteksi wajah yang tidak menggunakan masker adalah pekerjaan yang lama dan melelahkan. Computer vision merupakan salah satu cabang ilmu komputer yang dapat digunakan untuk klasifikasi citra. Convolutional Neural Network (CNN) merupakan algoritma deep learning yang memiliki performa bagus dalam klasifikasi citra. Transfer learning merupakan metode terkini untuk mempercepat waktu training pada CNN dan untuk mendapatkan performa klasifikasi yang lebih baik. Penelitian ini melakukan klasifikasi citra wajah untuk membedakan orang menggunakan masker atau tidak dengan menggunakan CNN dan Transfer Learning. Arsitektur CNN yang digunakan dalam penelitian ini adalah MobileNetV2, VGG16, DenseNet201, dan Xception. Berdasarkan hasil uji coba menggunakan 5-cross validation, Xception memiliki akurasi terbaik yaitu 0.988 dengan waktu total komputasi training dan testing sebesar 18274 detik. MobileNetV2 memiliki waktu total komputasi tercepat yaitu 4081 detik dengan akurasi sebesar 0.981.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>In 2021 the Covid-19 pandemic is still a problem in the world. Therefore, health protocols are needed to prevent the spread of Covid-19. The use of face masks is one of the commonly used health protocols. However, manually checking to detect faces that are not wearing masks is a long and tiring job. Computer vision is a branch of computer science that can be used for image classification. Convolutional Neural Network (CNN) is a deep learning algorithm that has good performance in image classification. Transfer learning is the latest method to speed up CNN training and get better classification performance. This study performs facial image classification to distinguish people using masks or not by using CNN and Transfer Learning. The CNN architecture used in this research is MobileNetV2, VGG16, DenseNet201, and Xception. Based on the results of trials using 5-cross validation, Xception has the best accuracy of 0.988 with a total computation time of training and testing of 18274 seconds. MobileNetV2 has the fastest total computing time of 4081 seconds with an accuracy of 0.981.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

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