Instructions to use yeonmorae/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yeonmorae/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="yeonmorae/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("yeonmorae/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("yeonmorae/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from yeonmorae/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/yeonmorae/cppe5_use_data_finetuning/resolve/16632efa3bfd92ff4c4d3541a08495296cf151d3/pytorch_model.bin
- Command line
-
hf download hf://yeonmorae/cppe5_use_data_finetuning@16632efa3bfd92ff4c4d3541a08495296cf151d3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/yeonmorae/cppe5_use_data_finetuning/resolve/16632efa3bfd92ff4c4d3541a08495296cf151d3/pytorch_model.bin
167 MB
- Xet hash:
- e70d58b499e11905d14145a1999c0d3a76d477fa673f65c9b2b93029c4aa82c9
- Size of remote file:
- 167 MB
- SHA256:
- 3de05120bf41245b08610b653f2c15e4c6caf69cf49773cdb0e152cd9a5e6680
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