unet的scales表示什么

### Residual U-Net Architecture in Deep Learning for Image Segmentation Residual connections, introduced to address the degradation problem observed when training very deep networks[^4], have been successfully integrated into various architectures including U-Net. The combination of residual blocks with U-Net leverages both high-resolution spatial information and low-level feature maps while mitigating vanishing gradient issues. In a **Residual U-Net**, each encoding block not only downsamples features but also includes skip connections that bypass one or more layers. These shortcuts allow gradients to flow through an alternative route during backpropagation, facilitating deeper network training without compromising performance: ```python def res_block(x, filters): shortcut = x conv1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=(3, 3), padding='same', activation='relu')(x) conv2 = tf.keras.layers.Conv2D(filters=filters, kernel_size=(3, 3), padding='same')(conv1) output = tf.keras.layers.Add()([shortcut, conv2]) return tf.keras.activations.relu(output) def build_res_unet(input_shape, num_classes=2): inputs = tf.keras.Input(shape=input_shape) # Encoder path (with residual blocks) c1 = res_block(inputs, 64) p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1) c2 = res_block(p1, 128) p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2) # Bottleneck b = res_block(p2, 256) # Decoder path (concatenation from encoder paths) u7 = tf.keras.layers.UpSampling2D((2, 2), interpolation="bilinear")(b) cat_7 = tf.keras.layers.Concatenate(axis=-1)([u7, c2]) d7 = res_block(cat_7, 128) u8 = tf.keras.layers.UpSampling2D((2, 2), interpolation="bilinear")(d7) cat_8 = tf.keras.layers.Concatenate(axis=-1)([u8, c1]) d8 = res_block(cat_8, 64) outputs = tf.keras.layers.Conv2D(num_classes, (1, 1), activation='softmax')(d8) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) return model ``` The integration of residuals within U-Net enhances its capability by allowing direct propagation of identity mappings alongside learned transformations at different scales. This design choice significantly improves convergence speed and final accuracy on complex tasks such as medical image segmentation[^2].

### GitHub Projects Combining Transformer and U-Net Architectures For projects that combine Transformer and U-Net architectures, several repositories stand out due to their innovative approaches in leveraging these models for various applications such as medical imaging, natural image processing, and more. One notable project is **Uformer**, which modifies traditional convolutional layers within a UNet-like framework into Transformer blocks while preserving the hierarchical encoder-decoder structure along with skip connections[^1]. This approach allows for better recovery of detailed features across multiple scales by utilizing self-attention mechanisms effectively. Another interesting development involves simplifying certain components like removing FFN/GLU structures from Transformers, similar modifications have been explored alongside other improvements in related research works [^2]. #### Example Project: Uformer Implementation on GitHub A specific example can be found through searching or browsing relevant tags/topics under GitHub. The official implementation repository associated with the aforementioned paper provides comprehensive codebase including training scripts, pretrained weights, etc., demonstrating how one could implement advanced techniques using PyTorch: ```python import torch.nn as nn class Uformer(nn.Module): def __init__(self, img_size=256, patch_size=16, in_chans=3, embed_dim=768, depth=[2, 2, 2], num_heads=12, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, act_layer=nn.GELU): super().__init__() # Define your model here... ``` This snippet shows part of what might constitute setting up such hybrid networks where elements typical to both Unets (like layer normalization) coexist seamlessly beside those characteristic ones seen inside transformers (multi-head attention).