June 21, 2022 Uncategorized Leave a comment

Swin Transformer supports 3-billion-parameter vision models that can train with higher-resolution images for greater task applicability

On the left, a diagram with three layers, each of which contains a half-transparent image processed from the same image. The processed images are partitioned into several grids, and each grid contains 4 x 4 image patches. From bottom to top, the number of grids in each layer are 4 x 4, 2 x 2, and 1 x 1, respectively. The layers are labeled “4x,” “8x,” and “16x,” respectively, from bottom to top. An arrow joining the three layers points upward to the words “Segmentation” and “Detection” and an ellipsis. Another arrow points from the top layer to the word “classification.” On the right, a bar chart with a blue bar labeled “Swin V1” and an orange bar

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