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_target_: text_recognizer.networks.ConvTransformer
input_dims: [1, 1, 576, 640]
hidden_dim: &hidden_dim 96
num_classes: 58
pad_index: 3
encoder:
_target_: text_recognizer.networks.EfficientNet
arch: b0
stochastic_dropout_rate: 0.2
bn_momentum: 0.99
bn_eps: 1.0e-3
depth: 5
out_channels: *hidden_dim
decoder:
_target_: text_recognizer.networks.transformer.Decoder
depth: 6
block:
_target_: text_recognizer.networks.transformer.DecoderBlock
self_attn:
_target_: text_recognizer.networks.transformer.Attention
dim: *hidden_dim
num_heads: 12
dim_head: 64
dropout_rate: &dropout_rate 0.2
causal: true
rotary_embedding:
_target_: text_recognizer.networks.transformer.RotaryEmbedding
dim: 64
cross_attn:
_target_: text_recognizer.networks.transformer.Attention
dim: *hidden_dim
num_heads: 8
dim_head: 64
dropout_rate: *dropout_rate
causal: false
norm:
_target_: text_recognizer.networks.transformer.RMSNorm
dim: *hidden_dim
ff:
_target_: text_recognizer.networks.transformer.FeedForward
dim: *hidden_dim
dim_out: null
expansion_factor: 2
glu: true
dropout_rate: *dropout_rate
pixel_embedding:
_target_: text_recognizer.networks.transformer.AxialPositionalEmbedding
dim: *hidden_dim
shape: [18, 79]
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