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--- |
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library_name: keras-hub |
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tags: |
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- image-segmentation |
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- keras |
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--- |
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## Model Overview |
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A Keras model implementing the MixTransformer architecture to be used as a backbone for the SegFormer 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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References: |
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- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) # noqa: E501 |
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- [Based on the TensorFlow implementation from DeepVision](https://github.com/DavidLandup0/deepvision/tree/main/deepvision/models/classification/mix_transformer) # noqa: E501 |
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## Links |
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* [MiT Quickstart Notebook: coming soon]() |
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* [MiT API Documentation: 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>=3 |
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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://dl.fbaipublicfiles.com/segment_anything/. Full code examples for each are available below. |
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Here's the table formatted similarly to the given pattern: |
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Here's the updated table with the input resolutions included in the descriptions: |
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| Preset name | Parameters | Description | |
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|--------------------------|------------|--------------------------------------------------------------------------------------------------| |
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| mit_b0_ade20k_512 | 3.32M | MiT (MixTransformer) model with 8 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. | |
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| mit_b1_ade20k_512 | 13.16M | MiT (MixTransformer) model with 8 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. | |
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| mit_b2_ade20k_512 | 24.20M | MiT (MixTransformer) model with 16 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. | |
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| mit_b3_ade20k_512 | 44.08M | MiT (MixTransformer) model with 28 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. | |
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| mit_b4_ade20k_512 | 60.85M | MiT (MixTransformer) model with 41 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. | |
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| mit_b5_ade20k_640 | 81.45M | MiT (MixTransformer) model with 52 transformer blocks, trained on the ADE20K dataset with an input resolution of 640x640 pixels. | |
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| mit_b0_cityscapes_1024 | 3.32M | MiT (MixTransformer) model with 8 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. | |
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| mit_b1_cityscapes_1024 | 13.16M | MiT (MixTransformer) model with 8 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. | |
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| mit_b2_cityscapes_1024 | 24.20M | MiT (MixTransformer) model with 16 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. | |
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| mit_b3_cityscapes_1024 | 44.08M | MiT (MixTransformer) model with 28 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. | |
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| mit_b4_cityscapes_1024 | 60.85M | MiT (MixTransformer) model with 41 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. | |
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| mit_b5_cityscapes_1024 | 81.45M | MiT (MixTransformer) model with 52 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. | |
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## Example Usage |
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Using the class with a `backbone`: |
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``` |
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import tensorflow as tf |
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import keras_cv |
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import numpy as np |
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images = np.ones(shape=(1, 96, 96, 3)) |
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labels = np.zeros(shape=(1, 96, 96, 1)) |
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backbone = keras_cv.models.MiTBackbone.from_preset("mit_b4_ade20k_512") |
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# Evaluate model |
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model(images) |
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# Train model |
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model.compile( |
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optimizer="adam", |
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loss=keras.losses.BinaryCrossentropy(from_logits=False), |
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metrics=["accuracy"], |
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) |
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model.fit(images, labels, epochs=3) |
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``` |
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## Example Usage with Hugging Face URI |
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Using the class with a `backbone`: |
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``` |
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import tensorflow as tf |
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import keras_cv |
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import numpy as np |
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images = np.ones(shape=(1, 96, 96, 3)) |
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labels = np.zeros(shape=(1, 96, 96, 1)) |
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backbone = keras_cv.models.MiTBackbone.from_preset("hf://keras/mit_b4_ade20k_512") |
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# Evaluate model |
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model(images) |
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# Train model |
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model.compile( |
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optimizer="adam", |
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loss=keras.losses.BinaryCrossentropy(from_logits=False), |
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metrics=["accuracy"], |
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) |
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model.fit(images, labels, epochs=3) |
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``` |
