Instructions to use kelligag/trainer_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kelligag/trainer_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="kelligag/trainer_output")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("kelligag/trainer_output") model = AutoModelForObjectDetection.from_pretrained("kelligag/trainer_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bcbabf1a465ca4348f65e68fd55c9de5ce6fdc4e72ef01a4ffdbbbe3456c0d4
- Size of remote file:
- 5.27 kB
- SHA256:
- 1a01333b2a48338186a6a6627e534fba9381815e4766245a5144233c39fccebc
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