Nowadays, analysis of transparent images in the field of computer vision has gradually become a hot spot. In this paper , we compare the classification performance of different deep learning  for the problem that transparent images are difficult to analyze. We crop the transparent images into 8 * 8 and 224 * 224 pixels patches in the same proportion , and then divide the two different pixels patches into foreground and background according to groundtruch . We also use 4 types of convolutional neural networks and a novel ViT network model to compare the foreground and background classification experiments. We conclude that ViT performs the worst in classifying 8 * 8 pixels patches, but it outperforms most convolutional neural networks in classifying 224 * 224.
Nowadays, analysis of Transparent Environmental URLanism Images (T-EM images) in the field of computer vision has gradually become a new and interesting spot. This paper compares different deep learning classification performance for the problem that T-EM images are challenging to analyze. We crop the T-EM images into 8 * 8 and 224 * 224 pixel patches in the same proportion and then divide the two different pixel patches into foreground and background according to ground truth . We also use four convolutional neural networks and a novel ViT network model to compare the foreground and background classification experiments. We conclude that ViT performs the worst in classifying 8 * 8 pixel patches, but it outperforms most convolutional neural networks in classifying 224 * 224 pixel patches.
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transparent images
Transparent Environmental URLanism Images (T-EM images)
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hot spot. In this paper , we compare the classification performance of
new and interesting spot. This paper compares
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classification performance
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transparent images are difficult
T-EM images are challenging
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transparent
T-EM
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pixels
pixel
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,
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pixels
pixel
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groundtruch
ground truth
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4 types of
four
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pixels
pixel
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224.
224 pixel patches.