Instructions to use dia218/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dia218/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="dia218/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("dia218/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("dia218/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dia218/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/dia218/cppe5_use_data_finetuning/resolve/0877e0b158cc55a8b5759973c00a22ee8ecdfd99/pytorch_model.bin
- Command line
-
hf download hf://dia218/cppe5_use_data_finetuning@0877e0b158cc55a8b5759973c00a22ee8ecdfd99/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dia218/cppe5_use_data_finetuning/resolve/0877e0b158cc55a8b5759973c00a22ee8ecdfd99/pytorch_model.bin
167 MB
- Xet hash:
- b4d53916f540334073f856c8f20e8bb3d192d338f8167dba8fb5d6e20b1d5b3e
- Size of remote file:
- 167 MB
- SHA256:
- c9b622fcc06fe43e9a528562cb4921db844cf5eb45f3ca2594b66f697a652cce
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