Instructions to use bin20/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bin20/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="bin20/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("bin20/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("bin20/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bin20/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/bin20/cppe5_use_data_finetuning/resolve/main/training_args.bin
- Command line
-
hf download hf://bin20/cppe5_use_data_finetuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bin20/cppe5_use_data_finetuning/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 62c6d977844d822e3cbf7482eb7e06f47bbedd500fb3bc8b296e833f168374dd
- Size of remote file:
- 4.54 kB
- SHA256:
- 2aec9f288b01f5980e7921a7181043f63d941e755549a629c5defa1876c6d72e
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