Keras 3 API documentation / KerasHub / Models API / ImageClassifierPreprocessor

ImageClassifierPreprocessor

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ImageClassifierPreprocessor class

keras_hub.models.ImageClassifierPreprocessor(image_converter=None, **kwargs)

Base class for image classification preprocessing layers.

ImageClassifierPreprocessor tasks wraps a keras_hub.layers.ImageConverter to create a preprocessing layer for image classification tasks. It is intended to be paired with a keras_hub.models.ImageClassifier task.

All ImageClassifierPreprocessor take inputs three inputs, x, y, and sample_weight. x, the first input, should always be included. It can be a image or batch of images. See examples below. y and sample_weight are optional inputs that will be passed through unaltered. Usually, y will be the classification label, and sample_weight will not be provided.

The layer will output either x, an (x, y) tuple if labels were provided, or an (x, y, sample_weight) tuple if labels and sample weight were provided. x will be the input images after all model preprocessing has been applied.

All ImageClassifierPreprocessor tasks include a from_preset() constructor which can be used to load a pre-trained config and vocabularies. You can call the from_preset() constructor directly on this base class, in which case the correct class for your model will be automatically instantiated.

Examples.

preprocessor = keras_hub.models.ImageClassifierPreprocessor.from_preset(
    "resnet_50",
)

# Resize a single image for resnet 50.
x = np.random.randint(0, 256, (512, 512, 3))
x = preprocessor(x)

# Resize a labeled image.
x, y = np.random.randint(0, 256, (512, 512, 3)), 1
x, y = preprocessor(x, y)

# Resize a batch of labeled images.
x, y = [np.random.randint(0, 256, (512, 512, 3)), np.zeros((512, 512, 3))], [1, 0]
x, y = preprocessor(x, y)

# Use a [`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset).
ds = tf.data.Dataset.from_tensor_slices((x, y)).batch(2)
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

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from_preset method

ImageClassifierPreprocessor.from_preset(
    preset, config_file="preprocessor.json", **kwargs
)

Instantiate a keras_hub.models.Preprocessor 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 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'

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

As there are usually multiple preprocessing classes for a given model, this method should be called on a specific subclass like keras_hub.models.BertTextClassifierPreprocessor.from_preset().

Arguments

  • preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.

Examples

# Load a preprocessor for Gemma generation.
preprocessor = keras_hub.models.GemmaCausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)

# Load a preprocessor for Bert classification.
preprocessor = keras_hub.models.BertTextClassifierPreprocessor.from_preset(
    "bert_base_en",
)
Preset name Parameters Description
densenet_121_imagenet 7.04M 121-layer DenseNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
densenet_169_imagenet 12.64M 169-layer DenseNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
densenet_201_imagenet 18.32M 201-layer DenseNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_18_imagenet 11.19M 18-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_50_imagenet 23.56M 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_101_imagenet 42.61M 101-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_152_imagenet 58.30M 152-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_v2_50_imagenet 23.56M 50-layer ResNetV2 model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_v2_101_imagenet 42.61M 101-layer ResNetV2 model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_vd_18_imagenet 11.72M 18-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_vd_34_imagenet 21.84M 34-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_vd_50_imagenet 25.63M 50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_vd_50_ssld_imagenet 25.63M 50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation.
resnet_vd_50_ssld_v2_imagenet 25.63M 50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation and AutoAugment.
resnet_vd_50_ssld_v2_fix_imagenet 25.63M 50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation, AutoAugment and additional fine-tuning of the classification head.
resnet_vd_101_imagenet 44.67M 101-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_vd_101_ssld_imagenet 44.67M 101-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation.
resnet_vd_152_imagenet 60.36M 152-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
resnet_vd_200_imagenet 74.93M 200-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
mit_b0_ade20k_512 3.32M MiT (MixTransformer) model with 8 transformer blocks.
mit_b1_ade20k_512 13.16M MiT (MixTransformer) model with 8 transformer blocks.
mit_b2_ade20k_512 24.20M MiT (MixTransformer) model with 16 transformer blocks.
mit_b3_ade20k_512 44.08M MiT (MixTransformer) model with 28 transformer blocks.
mit_b4_ade20k_512 60.85M MiT (MixTransformer) model with 41 transformer blocks.
mit_b5_ade20k_640 81.45M MiT (MixTransformer) model with 52 transformer blocks.
mit_b0_cityscapes_1024 3.32M MiT (MixTransformer) model with 8 transformer blocks.
mit_b1_cityscapes_1024 13.16M MiT (MixTransformer) model with 8 transformer blocks.
mit_b2_cityscapes_1024 24.20M MiT (MixTransformer) model with 16 transformer blocks.
mit_b3_cityscapes_1024 44.08M MiT (MixTransformer) model with 28 transformer blocks.
mit_b4_cityscapes_1024 60.85M MiT (MixTransformer) model with 41 transformer blocks.
mit_b5_cityscapes_1024 81.45M MiT (MixTransformer) model with 52 transformer blocks.
vgg_11_imagenet 9.22M 11-layer vgg model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
vgg_13_imagenet 9.40M 13-layer vgg model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
vgg_16_imagenet 14.71M 16-layer vgg model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.
vgg_19_imagenet 20.02M 19-layer vgg model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.

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save_to_preset method

ImageClassifier.save_to_preset(preset_dir)

Save task to a preset directory.

Arguments

  • preset_dir: The path to the local model preset directory.