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- Swin Transformer - GitHub
Swin Transformer (the name Swin stands for Shifted window) is initially described in arxiv, which capably serves as a general-purpose backbone for computer vision It is basically a hierarchical Transformer whose representation is computed with shifted windows The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while
- Swin Transformer - GitHub
SwinTransformer Swin-Transformer-Object-Detection’s past year of commit activity
- Swin-Transformer get_started. md at main - GitHub
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" - microsoft Swin-Transformer
- SwinTransformer Swin-Transformer-Object-Detection - GitHub
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation - SwinTransformer Swin-Transformer-Object-Detection
- SwinTransformer Swin-Transformer-Semantic-Segmentation
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation - SwinTransformer Swin-Transformer-Semantic-Segmentation
- GitHub - SwinTransformer Video-Swin-Transformer: This is an official . . .
This is an official implementation for "Video Swin Transformers" - SwinTransformer Video-Swin-Transformer
- berniwal swin-transformer-pytorch - GitHub
import torch from swin_transformer_pytorch import SwinTransformer net = SwinTransformer ( hidden_dim = 96, layers = (2, 2, 6, 2), heads = (3, 6, 12, 24), channels = 3, num_classes = 3, head_dim = 32, window_size = 7, downscaling_factors = (4, 2, 2, 2), relative_pos_embedding = True) dummy_x = torch randn (1, 3, 224, 224) logits = net (dummy_x
- GitHub - SwinTransformer Transformer-SSL: This is an official . . .
This is an official implementation for "Self-Supervised Learning with Swin Transformers" - SwinTransformer Transformer-SSL
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