RetinaNetBackbone model

[source]

RetinaNetBackbone class

keras_hub.models.RetinaNetBackbone(
    image_encoder,
    min_level,
    max_level,
    use_p5,
    use_fpn_batch_norm=False,
    image_shape=(None, None, 3),
    data_format=None,
    dtype=None,
    **kwargs
)

RetinaNet Backbone.

Combines a CNN backbone (e.g., ResNet, MobileNet) with a feature pyramid network (FPN)to extract multi-scale features for object detection.

Arguments

• image_encoder: keras.Model. The backbone model (e.g., ResNet50, MobileNetV2) used to extract features from the input image. It should have pyramid outputs (i.e., a dictionary mapping level names like "P2", "P3", etc. to their corresponding feature tensors).
• min_level: int. The minimum level of the feature pyramid (e.g., 3). This determines the coarsest level of features used.
• max_level: int. The maximum level of the feature pyramid (e.g., 7). This determines the finest level of features used.
• use_p5: bool. Determines the input source for creating coarser feature pyramid levels. If True, the output of the last backbone layer (typically 'P5' in an FPN) is used as input to create higher-level feature maps (e.g., 'P6', 'P7') through additional convolutional layers. If False, the original 'P5' feature map from the backbone is directly used as input for creating the coarser levels, bypassing any further processing of 'P5' within the feature pyramid. Defaults to False.
• use_fpn_batch_norm: bool. Whether to use batch normalization in the feature pyramid network. Defaults to False.
• image_shape: tuple. tuple. The shape of the input image (H, W, C). The height and width can be None if they are variable.
• data_format: str. The data format of the input image (channels_first or channels_last).
• dtype: str. The data type of the input image.
• **kwargs: Additional keyword arguments passed to the base class.

Raises

• ValueError: If min_level is greater than max_level.
• ValueError: If backbone_max_level is less than 5 and max_level is greater than or equal to 5.

[source]

from_preset method

RetinaNetBackbone.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 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,
)