Instructions to use timm/swin_base_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_base_patch4_window7_224.ms_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/swin_base_patch4_window7_224.ms_in1k", pretrained=True) - Transformers
How to use timm/swin_base_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_base_patch4_window7_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_base_patch4_window7_224.ms_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0415878f67d9aa9e56a5c7b2bff5f7ca65b693dc8793ae96ff8b37ad25513aa3
- Size of remote file:
- 353 MB
- SHA256:
- 2b53109ee9dfb4c8957ed9dc8417ffef3c16eb05c3240468df5f089c53f8d226
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