Instructions to use Jinhyeong99/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jinhyeong99/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Jinhyeong99/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Jinhyeong99/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("Jinhyeong99/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jinhyeong99/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/Jinhyeong99/cppe5_use_data_finetuning/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://Jinhyeong99/cppe5_use_data_finetuning@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jinhyeong99/cppe5_use_data_finetuning/resolve/refs%2Fpr%2F1/training_args.bin
4.54 kB
- Xet hash:
- a87740b649e3cd65d4fd4dcf353e55a828cfc2d128cff07ead1fbe4abedaa3ef
- Size of remote file:
- 4.54 kB
- SHA256:
- 977629d3c3868f1d8de02f27f85c09232ec8c0db78d0124d5007ffaa2fab3ea5
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