Instructions to use James332/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use James332/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="James332/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("James332/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("James332/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from James332/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/James332/cppe5_use_data_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://James332/cppe5_use_data_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/James332/cppe5_use_data_finetuning/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 1464ae4e30f42cea603558e8f55355d8fd39caf1a3fee21026ddd98d78b6d11e
- Size of remote file:
- 167 MB
- SHA256:
- 4c788868170a5fbed5d633a84698f7f156fa4a2cb8192c180b17ec9f75599194
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