Instructions to use taohungchang/trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taohungchang/trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="taohungchang/trained_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("taohungchang/trained_model") model = AutoModelForObjectDetection.from_pretrained("taohungchang/trained_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from taohungchang/trained_model: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/taohungchang/trained_model/resolve/main/training_args.bin
- Command line
-
hf download hf://taohungchang/trained_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/taohungchang/trained_model/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- d271ccb60ab0e8eb4763f3cb29cd49eeaed02265f2f29c69f90fa0700b649eeb
- Size of remote file:
- 3.9 kB
- SHA256:
- a1c8ac9d376b7563df88ea050b55c3a3bceb74eb32157667dcfeff61e1cc08b9
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