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Instantiates the Swin Transformer architecture.
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Instantiates the Swin Transformer architecture.
The Swin Transformer (Shifted Window Transformer) is a hierarchical vision transformer whose representation is computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection.
This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities make Swin Transformer compatible with a broad range of vision tasks, including image classification, object detection, and semantic segmentation.
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1 sourceVérifié 10 août
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2 artefactsExtraits de sources
3 extraitsUnlike traditional Vision Transformers (ViT), which compute attention globally across all patches (resulting in quadratic complexity relative to image size), Swin Transformer computes self-attention within local non-overlapping windows. By shifting the window partition between consecutive layers, the model achieves cross-window connections, maintaining linear computational complexity while enabling robust global context modeling.
Keras and KerasHub can be installed with:
pip install -U -q keras-hub
pip install -U -q keras
JAX, TensorFlow, and PyTorch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment, see the Keras Getting Started page.
The following model checkpoints are provided by the Keras team. Weights have been ported from Hugging Face Hub.
| Preset name | Parameters | Description |
|---|---|---|
| swin_tiny_patch4_window7_224 | 28.29M | Tiny Swin Transformer model pre-trained on ImageNet-1k at a 224x224 resolution |
| swin_small_patch4_window7_224 | 49.61M | Small Swin Transformer model pre-trained on ImageNet-1k at a 224x224 resolution |
| swin_base_patch4_window7_224 | 87.77M | Base Swin Transformer model pre-trained on ImageNet-1k at a 224x224 resolution |
| swin_base_patch4_window12_384 | 87.90M | Base Swin Transformer model pre-trained on ImageNet-1k at a 384x384 resolution |
| swin_large_patch4_window7_224 | 196.53M | Large Swin Transformer model pre-trained ImageNet-1k at a 224x224 resolution |
| swin_large_patch4_window12_384 | 196.74M | Large Swin Transformer model pre-trained on ImageNet-1k at a 384x384 resolution |
import numpy as np
import keras_hub
# Pretrained Swin Transformer backbone
model = keras_hub.models.SwinTransformerBackbone.from_preset("swin_tiny_patch4_window7_224")
input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
model(input_data)
# Randomly initialized Swin Transformer backbone with custom config
model = keras_hub.models.SwinTransformerBackbone(
image_shape=(224, 224, 3),
embed_dim=96,
depths=(2, 2, 6, 2),
num_heads=(3, 6, 12, 24),
window_size=7,
)
model(input_data)
# Use Swin Transformer for image classification task
classifier = keras_hub.models.SwinTransformerImageClassifier.from_preset(
"swin_tiny_patch4_window7_224",
num_classes=1000,
)
# Use Hugging Face presets directly for on-the-fly conversion
classifier = keras_hub.models.SwinTransformerImageClassifier.from_preset(
"hf://microsoft/swin-tiny-patch4-window7-224"
)
import numpy as np
import keras_hub
# Top-5 ImageNet class decoding.
model = keras_hub.models.SwinTransformerImageClassifier.from_preset(
"swin_base_patch4_window7_224"
)
images = np.random.randint(0, 256, size=(1, 384, 384, 3), dtype="uint8")
logits = model.predict(images, verbose=0)
print(keras_hub.utils.decode_imagenet_predictions(logits, top=5)[0])
import numpy as np
import keras_hub
# Top-5 ImageNet class decoding.
model = keras_hub.models.SwinTransformerImageClassifier.from_preset(
"hf://keras/swin_base_patch4_window7_224"
)
images = np.random.randint(0, 256, size=(1, 384, 384, 3), dtype="uint8")
logits = model.predict(images, verbose=0)
print(keras_hub.utils.decode_imagenet_predictions(logits, top=5)[0])
--- library_name: keras-hub --- ### Model Overview # Swin Transformer Instantiates the Swin Transformer architecture. ## Model Details The Swin Transformer (Shifted Window Transformer) is a hierarchical vision transformer whose representation is computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities make Swin Transformer compatible with a broad range of vision tasks, including image classification, object detection, and semantic segmentation. ### Reference * [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) Unlike traditional Vision Transformers (ViT), which compute attention globally across all patches (resulting in quadratic complexity relative to image size), Swin Transformer computes self-attention within local non-overlapping windows. By shifting the window partition between consecutive layers, the model achieves cross-window connections, maintaining linear computational complexity while enabling robust global context modeling. ### Links * [Swin Transformer Quickstart Notebook](https://www.kaggle.com/code/prasadsachin/swin-transformer-quickstart-keras-hub) * [Swin Transformer API Documentation](https://keras.io/keras_hub/api/models/swin_transformer/) * [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/) * [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/) ## Installation Keras and KerasHub can be installed with: ```bash pip install -U -q keras-hub pip install -U -q keras ``` JAX, TensorFlow, and PyTorch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment, see the [Keras Getting Started](https://keras.io/getting_started/) page. ## Presets The following model checkpoints are provided by the Keras team. Weights have been ported from [Hugging Face Hub](https://huggingface.co/microsoft). | Preset name | Parameters | Description | | :--- | :--- | :--- | | **swin_tiny_patch4_window7_224** | 28.29M | Tiny Swin Transformer model pre-trained on ImageNet-1k at a 224x224 resolution | | **...
Source context: 3 downloads · 0 likes · Library keras-hub · Repo keras/swin_base_patch4_window7_224
Source context: 2 downloads · 0 likes · Library keras-hub · Repo keras/swin_base_patch4_window7_224