Instructions to use Abhiram4/SwinMark2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhiram4/SwinMark2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Abhiram4/SwinMark2") 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("Abhiram4/SwinMark2") model = AutoModelForImageClassification.from_pretrained("Abhiram4/SwinMark2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Abhiram4/SwinMark2: direct link, hf CLI and curl.
- Browser
- Download file 110 MB
-
https://huggingface.co/Abhiram4/SwinMark2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Abhiram4/SwinMark2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Abhiram4/SwinMark2/resolve/main/pytorch_model.bin
110 MB
- Xet hash:
- 5979eb6324b24bd6c46bf1ebad639bdd13db196fdc38b42a624691eaf1159cca
- Size of remote file:
- 110 MB
- SHA256:
- 828d5ae0514cb6793bd6ed8dfad82c93606192eaa2d95f6864b25d3b7e6003c2
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