Instructions to use jin5605/Jins_cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jin5605/Jins_cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="jin5605/Jins_cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("jin5605/Jins_cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("jin5605/Jins_cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jin5605/Jins_cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/jin5605/Jins_cppe5_use_data_finetuning/resolve/main/training_args.bin
- Command line
-
hf download hf://jin5605/Jins_cppe5_use_data_finetuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jin5605/Jins_cppe5_use_data_finetuning/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- c21b5b495bc08a489a7cd4432cc657b8b64b69c86b531b799645d9ffef018e30
- Size of remote file:
- 4.6 kB
- SHA256:
- 3d96582231eb0514ea2e8d36a6ff80b2424b43584c41f9495286f0d884ed1918
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