Instructions to use DDingcheol/ToDoTaskResult with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DDingcheol/ToDoTaskResult with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="DDingcheol/ToDoTaskResult")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("DDingcheol/ToDoTaskResult") model = AutoModelForObjectDetection.from_pretrained("DDingcheol/ToDoTaskResult", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 440ecdc021011cbdbc2e0987200274d1e4a9bf00c2c9efcb646cbb720d9c891c
- Size of remote file:
- 4.47 kB
- SHA256:
- 071699b4feda1004ec9c4c2ce154d12c6fdc81ad83c53fc7d8c9710ac2192a87
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