Instructions to use bin20/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bin20/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="bin20/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("bin20/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("bin20/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bin20/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/bin20/cppe5_use_data_finetuning/resolve/8a7ca063685adae400a7a8cc984e68a1ed5368b7/pytorch_model.bin
- Command line
-
hf download hf://bin20/cppe5_use_data_finetuning@8a7ca063685adae400a7a8cc984e68a1ed5368b7/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bin20/cppe5_use_data_finetuning/resolve/8a7ca063685adae400a7a8cc984e68a1ed5368b7/pytorch_model.bin
167 MB
- Xet hash:
- 358f36e143ff191d2b4a4ceb8ff1538ed096487006c49ba9d2ccdf4fa8339797
- Size of remote file:
- 167 MB
- SHA256:
- 89aa23716ddef9519cf133a0ed142efe00748d18764662d176190ceeb910a6b4
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