Instructions to use timm/swin_small_patch4_window7_224.ms_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/swin_small_patch4_window7_224.ms_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/swin_small_patch4_window7_224.ms_in1k", pretrained=True) - Transformers
How to use timm/swin_small_patch4_window7_224.ms_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/swin_small_patch4_window7_224.ms_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("timm/swin_small_patch4_window7_224.ms_in1k") model = AutoModelForImageClassification.from_pretrained("timm/swin_small_patch4_window7_224.ms_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/swin_small_patch4_window7_224.ms_in1k: direct link, hf CLI and curl.
- Browser
- Download file 200 MB
-
https://huggingface.co/timm/swin_small_patch4_window7_224.ms_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/swin_small_patch4_window7_224.ms_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/swin_small_patch4_window7_224.ms_in1k/resolve/main/pytorch_model.bin
200 MB
- Xet hash:
- 0d368288b8e32c21a7d2a7a63746b7939bf5ff37f8eb8fd6ab74acb322ffcd1a
- Size of remote file:
- 200 MB
- SHA256:
- afaa9bc944b15e61116af545de21de9b263cee9ac41fc72655ee36a336ff9725
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