Instructions to use timm/swin_small_patch4_window7_224.ms_in22k 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_in22k with timm:
import timm model = timm.create_model("hf-hub:timm/swin_small_patch4_window7_224.ms_in22k", pretrained=True) - Transformers
How to use timm/swin_small_patch4_window7_224.ms_in22k 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_in22k") 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_in22k") model = AutoModelForImageClassification.from_pretrained("timm/swin_small_patch4_window7_224.ms_in22k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/swin_small_patch4_window7_224.ms_in22k: direct link, hf CLI and curl.
- Browser
- Download file 264 MB
-
https://huggingface.co/timm/swin_small_patch4_window7_224.ms_in22k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/swin_small_patch4_window7_224.ms_in22k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/swin_small_patch4_window7_224.ms_in22k/resolve/main/pytorch_model.bin
264 MB
- Xet hash:
- 74bdf002621e3b0aaf161cf5f50db6ada9ba6f127cbc7586e27e68648a775109
- Size of remote file:
- 264 MB
- SHA256:
- 810adb09b98695c2507702e1baa5dfabae3760f716dec48b4c26c5d633485587
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