Instructions to use timm/swin_base_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_base_patch4_window7_224.ms_in22k with timm:
import timm model = timm.create_model("hf_hub:timm/swin_base_patch4_window7_224.ms_in22k", pretrained=True) - Transformers
How to use timm/swin_base_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_base_patch4_window7_224.ms_in22k") 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_base_patch4_window7_224.ms_in22k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb066152a1a2c2d013c6f32bd43ee9b947e2514b91831be4d6174fa8abd681b4
- Size of remote file:
- 438 MB
- SHA256:
- 2f1563e5842091ea383b905a79d9a268f2996ac15ef1318efea64e18f822bd96
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