Instructions to use William0609/trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use William0609/trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="William0609/trained_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("William0609/trained_model") model = AutoModelForObjectDetection.from_pretrained("William0609/trained_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from William0609/trained_model: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/William0609/trained_model/resolve/main/training_args.bin
- Command line
-
hf download hf://William0609/trained_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/William0609/trained_model/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- 3daaeb18868f01dff76188f213ae2601cafcf561e3e1547cc136d5dde7349fd6
- Size of remote file:
- 3.9 kB
- SHA256:
- 322a3f5daa7ee597947cc31a271c9b0f835777af92cb6f578f3c1021ff18f546
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