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Suppose that I have 10K images of sizes $2400 \\times 2400$ to train a CNN How do I handle such large image sizes without downsampling? Here are a few more specific questions Are there any tech
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Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations Equivalently, an FCN is a CNN without fully connected layers Convolution neural networks The typical convolution neural network (CNN) is not fully convolutional because it often contains fully connected layers too (which do not perform the
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The concept of CNN itself is that you want to learn features from the spatial domain of the image which is XY dimension So, you cannot change dimensions like you mentioned
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The exam consists of 60 questions, requiring 70% to pass, and you have 1 hour 15 minutes per attempt CCNA 1 – Introduction to Networks (Version 7 00) – ITNv7 Course Final Exam Answers Full CCNA: Introduction to Networks Course Final Exam 1 Which two traffic types use the Real-Time Transport Protocol (RTP)? (Choose two )
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Is the image taken from a constant distance? If yes, you'd need to scale the images to the same dimensions first of all For few images say 100-500 images (more the better) you'd need to label the dataset by proper scaling Once labeled, use it to train a CNN (Although best would be training a ResNet) Once trained with decent accuracy, test it for the rest of your dataset I did something
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