Instructions to use timm/swin_tiny_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_tiny_patch4_window7_224.ms_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/swin_tiny_patch4_window7_224.ms_in1k", pretrained=True) - Transformers
How to use timm/swin_tiny_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_tiny_patch4_window7_224.ms_in1k", device_map="auto") 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_tiny_patch4_window7_224.ms_in1k", dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a35c1fae01ea2ecebb12174ade0d22cb69293a7d2cbb02ee554a3ac7024f049
- Size of remote file:
- 114 MB
- SHA256:
- 4d7d61fb43a9093e1deb6b9d6216000677ae8920e6fe4d42bfdde546d93aa833
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