Instructions to use dexforint/train_result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexforint/train_result with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="dexforint/train_result")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("dexforint/train_result") model = AutoModelForObjectDetection.from_pretrained("dexforint/train_result") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d214052b9f021dccc51c84acf66466043b1c06b86f7af6eadb4609435e8925c
- Size of remote file:
- 167 MB
- SHA256:
- 9b4008a48d16cae56dde83b717f8c694881c468e2b4ca5a1ea46c60b8f286b13
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