Instructions to use tanukinet/hanko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanukinet/hanko with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="tanukinet/hanko")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("tanukinet/hanko") model = AutoModelForObjectDetection.from_pretrained("tanukinet/hanko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tanukinet/hanko: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/tanukinet/hanko/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tanukinet/hanko/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tanukinet/hanko/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 5833ac754d374eb824f048b02cc74c78059014cab6d3b60c2104dad08d1005f6
- Size of remote file:
- 167 MB
- SHA256:
- 1d13efb473a68f124c3d2a27c2ae10804ab839ff0857267f05503092ef661c02
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