PARSeqBackbone model

[source]

PARSeqBackbone class

keras_hub.models.PARSeqBackbone(
    image_encoder,
    vocabulary_size,
    max_label_length,
    decoder_hidden_dim,
    num_decoder_layers,
    num_decoder_heads,
    decoder_mlp_dim,
    dropout_rate=0.1,
    attention_dropout=0.1,
    dtype=None,
    **kwargs
)

Scene Text Detection with PARSeq.

Performs OCR in natural scenes using the PARSeq model described in Scene Text Recognition with Permuted Autoregressive Sequence Models. PARSeq is a ViT-based model that allows iterative decoding by performing an autoregressive decoding phase, followed by a refinement phase.

Arguments

• image_encoder: keras.Model. The image encoder model.
• vocabulary_size: int. The size of the vocabulary.
• max_label_length: int. The maximum length of the label sequence.
• decoder_hidden_dim: int. The dimension of the decoder hidden layers.
• num_decoder_layers: int. The number of decoder layers.
• num_decoder_heads: int. The number of attention heads in the decoder.
• decoder_mlp_dim: int. The dimension of the decoder MLP hidden layer.
• dropout_rate: float. The dropout rate for the decoder network. Defaults to 0.1.
• attention_dropout: float. The dropout rate for the attention weights. Defaults to 0.1.
• dtype: str. None, str, or keras.mixed_precision.DTypePolicy. The dtype to use for the computations and weights.
• **kwargs: Additional keyword arguments passed to the base keras.Model constructor.

[source]

from_preset method

PARSeqBackbone.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_hub.models.Backbone from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as a one of:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a ModelScope handle like 'modelscope://user/bert_base_en'
5. a path to a local preset directory like './bert_base_en'

This constructor can be called in one of two ways. Either from the base class like keras_hub.models.Backbone.from_preset(), or from a model class like keras_hub.models.GemmaBackbone.from_preset(). If calling from the base class, the subclass of the returning object will be inferred from the config in the preset directory.

For any Backbone subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If True, the weights will be loaded into the model architecture. If False, the weights will be randomly initialized.

Examples

# Load a Gemma backbone with pre-trained weights.
model = keras_hub.models.Backbone.from_preset(
    "gemma_2b_en",
)

# Load a Bert backbone with a pre-trained config and random weights.
model = keras_hub.models.Backbone.from_preset(
    "bert_base_en",
    load_weights=False,
)