Instructions to use ryul99/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ryul99/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ryul99/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("ryul99/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("ryul99/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ryul99/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/ryul99/cppe5_use_data_finetuning/resolve/e4e770bd695bcaaaa5cb27ad00fc966fd7ed5819/pytorch_model.bin
- Command line
-
hf download hf://ryul99/cppe5_use_data_finetuning@e4e770bd695bcaaaa5cb27ad00fc966fd7ed5819/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ryul99/cppe5_use_data_finetuning/resolve/e4e770bd695bcaaaa5cb27ad00fc966fd7ed5819/pytorch_model.bin
167 MB
- Xet hash:
- 251549c095e7187ef58bb2a15509a97e3d0334fca20bca31b59bcbe7dfcb9c80
- Size of remote file:
- 167 MB
- SHA256:
- 1ad2d01bcd065a5b9767f4da0a543682abbb28a0d8c4d8ce115dbeee8eab8df1
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