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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from DDingcheol/ToDoTaskResult: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/DDingcheol/ToDoTaskResult/resolve/aa405dbe635995fe95dbb341caed721e7e07331e/pytorch_model.bin
- Command line
-
hf download hf://DDingcheol/ToDoTaskResult@aa405dbe635995fe95dbb341caed721e7e07331e/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DDingcheol/ToDoTaskResult/resolve/aa405dbe635995fe95dbb341caed721e7e07331e/pytorch_model.bin
167 MB
- Xet hash:
- 4ae8f49b91f901be2d01655b55906068833088208da94e7e7cc58b702dfc4c40
- Size of remote file:
- 167 MB
- SHA256:
- d56383961cb763bf4314b65195f0790b2fd256e3736fd5128d50e6bcc9329d58
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