Computer Vision Accuracy Analysis with Deep Learning Model Using TensorFlow
Keywords:
Accuracy, TensorFlow, GPU, Deep Learning, CNN, Classification, Prediction, Python, RELU, Backpropagation, Pooling, Flattening, Loss, Gradient DescentAbstract
Deep learning has absolutely dominated computer vision with creating a model that most accurately classifies the given image in the dataset and surpassing human performance. In previous research works many deep learning models are created and tested for image Classification on various datasets like MNIST, CIFAR-10, ImageNet using Python. Though they got good results of Accuracy for Classification, in this paper I have extended the work of measuring the performance analysis of Accuracy for Classification and also for the Predictions on CPU and GPU using TensorFlow2.0 and Keras on CIFAR-10 dataset having 50000 images of 10 datasets having a lot of different classes with very low resolutions. TensorFlow is an emerging technology on top of Python libraries developed by Google. This work reached an Accuracy 85% on GPU of Intel® Core™ i3- 7100U CPU which is acceptable with datasets used in this work are not easy to deal and all with very low resolutions having a lot of classes. That’s why it’s impacting the performance of the network. To classify and predict very low-resolution images from more datasets is really challenging one, it’s a great thing the computer vision accuracy performed excellent in my work.
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