Extracting image features through deep learning
The purpose of this study is to identify images with deep learning with the least error. In machine learning projects, the basis of the work is extracting features from raw data. Finally, we differentiate different features through classifiers. In the present project, images with dimensions of 224*224 are applied to the network. Most networks use color images, which have 3 channels, the final dimensions of which are 3*224*224. We used the vgg19 network to extract the feature from the image with the highest accuracy. To increase the speed of weight correction operations, batch_size = 30 is considered. 70% of the images were used for network training, 20% for validation and 10% of the data for network testing and evaluation. The speed and accuracy of this project is high.
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