Instructions to use timm/swin_s3_tiny_224.ms_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/swin_s3_tiny_224.ms_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/swin_s3_tiny_224.ms_in1k", pretrained=True) - Transformers
How to use timm/swin_s3_tiny_224.ms_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/swin_s3_tiny_224.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/swin_s3_tiny_224.ms_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af61b26aa73992ad7f41483053334207eb55416dd4b07210bba2fddf7628be36
- Size of remote file:
- 116 MB
- SHA256:
- b12969f0c97d3d636a61cf6f66b825326ae5a1b5fc490866ab29c48cc14aee7a
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