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--- |
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library_name: keras-hub |
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license: apache-2.0 |
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tags: |
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- image-classification |
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pipeline_tag: image-classification |
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--- |
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### Model Overview |
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Instantiates the ResNetV2 architecture. This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub. |
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**Reference** |
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- [Identity Mappings in Deep Residual Networks](https://arxiv.org/abs/1603.05027) (ECCV 2016) |
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The difference in Resnet and ResNetV2 rests in the structure of their |
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individual building blocks. In ResNetV2, the batch normalization and |
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ReLU activation precede the convolution layers, as opposed to ResNetV1 where |
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the batch normalization and ReLU activation are applied after the |
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convolution layers. |
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For transfer learning use cases, make sure to read the |
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[guide to transfer learning & fine-tuning](https://keras.io/guides/transfer_learning/). |
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## Links |
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coming soon |
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## Installation |
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Keras and KerasHub can be installed with: |
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``` |
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pip install -U -q keras-hub |
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pip install -U -q keras |
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``` |
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Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page. |
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## Presets |
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The following model checkpoints are provided by the Keras team. Weights have been ported from: https://huggingface.co/timm. Full code examples for each are available below. |
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| Preset name | Parameters | Description | |
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|------------------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| resnet_v2_50_imagenet | 23.56M | 50-layer ResNetV2 model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. | |
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| resnet_v2_101_imagenet | 42.61M | 101-layer ResNetV2 model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. | |
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## Example Usage |
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```python |
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# Pretrained ResNet backbone. |
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model = keras_hub.models.ResNetBackbone.from_preset("resnet_v2_101_imagenet") |
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input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3)) |
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model(input_data) |
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# Randomly initialized ResNetV2 backbone with a custom config. |
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model = keras_hub.models.ResNetBackbone( |
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input_conv_filters=[64], |
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input_conv_kernel_sizes=[7], |
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stackwise_num_filters=[64, 64, 64], |
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stackwise_num_blocks=[2, 2, 2], |
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stackwise_num_strides=[1, 2, 2], |
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block_type="basic_block", |
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use_pre_activation=True, |
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) |
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model(input_data) |
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# Use resnet for image classification task |
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model = keras_hub.models.ImageClassifier.from_preset("resnet_v2_101_imagenet") |
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# User timm presets directly from hugingface |
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model = keras_hub.models.ImageClassifier.from_preset('hf://timm/resnetv2_101.a1h_in1k') |
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``` |
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## Example Usage with Hugging Face URI |
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```python |
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# Pretrained ResNet backbone. |
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model = keras_hub.models.ResNetBackbone.from_preset("hf://keras/resnet_v2_101_imagenet") |
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input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3)) |
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model(input_data) |
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# Randomly initialized ResNetV2 backbone with a custom config. |
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model = keras_hub.models.ResNetBackbone( |
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input_conv_filters=[64], |
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input_conv_kernel_sizes=[7], |
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stackwise_num_filters=[64, 64, 64], |
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stackwise_num_blocks=[2, 2, 2], |
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stackwise_num_strides=[1, 2, 2], |
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block_type="basic_block", |
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use_pre_activation=True, |
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) |
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model(input_data) |
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# Use resnet for image classification task |
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model = keras_hub.models.ImageClassifier.from_preset("hf://keras/resnet_v2_101_imagenet") |
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# User timm presets directly from hugingface |
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model = keras_hub.models.ImageClassifier.from_preset('hf://timm/resnetv2_101.a1h_in1k') |
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``` |
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