Compact Convolutional Transformer
- class vformer.models.classification.cct.CCT(img_size=224, patch_size=4, in_channels=3, seq_pool=True, embedding_dim=768, num_layers=1, head_dim=96, num_heads=1, mlp_ratio=4.0, n_classes=1000, p_dropout=0.1, attn_dropout=0.1, drop_path=0.1, positional_embedding='learnable', decoder_config=(768, 1024), pooling_kernel_size=3, pooling_stride=2, pooling_padding=1)[source]
Implementation of Escaping the Big Data Paradigm with Compact Transformers
- Parameters
img_size (int) – Size of the image
patch_size (int) – Size of the single patch in the image
in_channels (int) – Number of input channels in image
seq_pool (bool) – Whether to use sequence pooling or not
embedding_dim (int) – Patch embedding dimension
num_layers (int) – Number of Encoders in encoder block
num_heads (int) – Number of heads in each transformer layer
mlp_ratio (float) – Ratio of mlp heads to embedding dimension
n_classes (int) – Number of classes for classification
p_dropout (float) – Dropout probability
attn_dropout (float) – Dropout probability
drop_path (float) – Stochastic depth rate, default is 0.1
positional_embedding (str) – One of the string values {
'learnable','sine',None}, default is'learnable'.decoder_config (tuple(int) or int) – Configuration of the decoder. If None, the default configuration is used.
pooling_kernel_size (int or tuple(int)) – Size of the kernel in MaxPooling operation
pooling_stride (int or tuple(int)) – Stride of MaxPooling operation
pooling_padding (int) – Padding in MaxPooling operation