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 training_args.bin from tanukinet/hanko: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/tanukinet/hanko/resolve/main/training_args.bin
- Command line
-
hf download hf://tanukinet/hanko/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tanukinet/hanko/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 94b3fd2311a281738857aafeaa005db8845280fc38e0c8a363be8480965ec649
- Size of remote file:
- 4.03 kB
- SHA256:
- a179b525cba537f37fa279ea19cae4fac225f02ca0ed250269c143d28c7cf5fc
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