Instructions to use OhST/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OhST/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="OhST/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("OhST/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("OhST/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from OhST/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/OhST/cppe5_use_data_finetuning/resolve/b7dbf93ca5482aeff32df907c0ec09203be3feb7/pytorch_model.bin
- Command line
-
hf download hf://OhST/cppe5_use_data_finetuning@b7dbf93ca5482aeff32df907c0ec09203be3feb7/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OhST/cppe5_use_data_finetuning/resolve/b7dbf93ca5482aeff32df907c0ec09203be3feb7/pytorch_model.bin
167 MB
- Xet hash:
- 4f92475664e329fa347d58266f4ff17e18dc948e28203cae37c32fd0d6881adc
- Size of remote file:
- 167 MB
- SHA256:
- 24393689a09186b68d9883a9dde700a583cf3c79a4f894677c594a5536cbdda8
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