ImageClassifierPreprocessor
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)
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:
- a built-in preset identifier like
'bert_base_en' - a Kaggle Models handle like
'kaggle://user/bert/keras/bert_base_en' - a Hugging Face handle like
'hf://user/bert_base_en' - 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. |
| 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. |
| 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. |
| 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. |
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.