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 model.safetensors from bin20/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 166 MB
-
https://huggingface.co/bin20/cppe5_use_data_finetuning/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://bin20/cppe5_use_data_finetuning@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/bin20/cppe5_use_data_finetuning/resolve/refs%2Fpr%2F1/model.safetensors
166 MB
- Xet hash:
- c6e28a2b337045143dab73c020b1e8871b532af886ddb368a4a58895700e9eb8
- Size of remote file:
- 166 MB
- SHA256:
- 693e7bd5e4499b473b3181b1a4d2b12852f4963c0f2b7bc2e549fb013ff8a56c
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