Instructions to use unojcn9f/screenlist-slicing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unojcn9f/screenlist-slicing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="unojcn9f/screenlist-slicing")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("unojcn9f/screenlist-slicing") model = AutoModelForObjectDetection.from_pretrained("unojcn9f/screenlist-slicing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a27320e52db0b7f15fa0a2511a3f795075f0feda2416ef2a47f91868c72dfd4b
- Size of remote file:
- 116 MB
- SHA256:
- faa17d85f186b33b8715c654cc06b05071e6a403526786306718c44a93af630b
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