Instructions to use timm/swinv2_tiny_window16_256.ms_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/swinv2_tiny_window16_256.ms_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/swinv2_tiny_window16_256.ms_in1k", pretrained=True) - Transformers
How to use timm/swinv2_tiny_window16_256.ms_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/swinv2_tiny_window16_256.ms_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/swinv2_tiny_window16_256.ms_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d63dfdf0814f8ef9633e537306163fd2abc0265bdeeea07de4c30bf244ae792
- Size of remote file:
- 119 MB
- SHA256:
- 5431a638f8d44f1ea1d67cd65f9d46e05c28eb34b35d697b9739df9beb7fc4d2
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