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:
- ff914ac0e715e19a5acba9dca3f9ab9006d522312f42cb59ee2feb8ccca41832
- Size of remote file:
- 167 MB
- SHA256:
- 45c5eb4c9dbe4efc449c82a5d708c4234489759b3f97d3afd11b7d2d90275d8f
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