Instructions to use microsoft/swinv2-tiny-patch4-window16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swinv2-tiny-patch4-window16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swinv2-tiny-patch4-window16-256") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window16-256") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window16-256") - Inference
- Notebooks
- Google Colab
- Kaggle
Update config.json
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by sipie800 - opened
- config.json +1 -1
config.json
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"num_layers": 4,
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"patch_size": 4,
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"pretrained_window_sizes": [
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"num_layers": 4,
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"patch_size": 4,
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"patch_norm": true,
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"pretrained_window_sizes": [
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