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:
- f10d22f39446a006cd35892723e8beda99389acb8c5dbf868346b6e44049cd03
- Size of remote file:
- 166 MB
- SHA256:
- 501b77ad2734cd1ab45d7765eef8ac8527d40bc9459e412aed95de3f8e060805
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