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Deep Learning in State-of-the-Art Image Classification Exceeding 99 Accuracy

机译:最先进的图像分类的深度学习超过99%的准确性

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Automatic image recognition and classification is a field of science that became popular in the recent years. Free platforms as Google Collaboratory make machine learning experiments more available to perform for everyone. The current technology enables us to use image recognition in such domains as medicine, criminology, entertainment or trading. In our research we created a state-of-the-art image classifier based on convolutional neural network model to classify ten models of old polish cars. We elaborated eight step training to fine tune the neural network. As the first step the data augmentation and precomputed activations were enabled. After that we froze all the layers but the last one, found the proper learning rate and performed interactive training. Fine-tuning, proper training and appropriate data preparation brought great results. The accuracy of cars' model recognition exceeded 99% with some room for improvement.
机译:自动图像识别和分类是近年来流行的科学领域。免费平台作为谷歌协作制作机器学习实验更适合每个人。目前的技术使我们能够在这种域中使用图像识别作为医学,犯罪学,娱乐或交易。在我们的研究中,我们创建了一种基于卷积神经网络模型的最先进的图像分类器,以分类十型旧波兰汽车。我们详细说明了八步训练来微调神经网络。作为第一步,启用了数据增强和预先计算的激活。之后我们冻结了所有层,但最后一个层,发现了适当的学习率并进行了交互式培训。微调,适当的培训和适当的数据准备带来了很大的结果。汽车模型识别的准确性超过99%,有一些改进的空间。

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