Instructions to use braaibander/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use braaibander/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="braaibander/outputs")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("braaibander/outputs") model = AutoModelForObjectDetection.from_pretrained("braaibander/outputs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from braaibander/outputs: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/braaibander/outputs/resolve/main/training_args.bin
- Command line
-
hf download hf://braaibander/outputs/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/braaibander/outputs/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 6906d4e04312975ce56b9acd90173856616515254968e7842dd4f4eb91b3ccaa
- Size of remote file:
- 5.37 kB
- SHA256:
- a1e77cf6022136423b487f3dfeea15177cbfc2900e713ef7c54e7064494a4e48
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