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/427ba609f9bbb852965759320ec90c3600a5af4e/pytorch_model.bin
- Command line
-
hf download hf://ryul99/cppe5_use_data_finetuning@427ba609f9bbb852965759320ec90c3600a5af4e/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ryul99/cppe5_use_data_finetuning/resolve/427ba609f9bbb852965759320ec90c3600a5af4e/pytorch_model.bin
167 MB
- Xet hash:
- b8dd83f57d6d5bca45586e79388fc94635cf11e6d49ac299ed6c10950c209fe5
- Size of remote file:
- 167 MB
- SHA256:
- 1c2c4745de7f50ad4b16b75515cb28dffdfd5cdefd87b2d6f7ebbf7d59ab8ae3
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