Instructions to use lknjbvhgjhibkhvj/keklol123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lknjbvhgjhibkhvj/keklol123 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="lknjbvhgjhibkhvj/keklol123")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("lknjbvhgjhibkhvj/keklol123") model = AutoModelForObjectDetection.from_pretrained("lknjbvhgjhibkhvj/keklol123") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f024208cba32763c71bae84e0fae40a31d19764eca3cfbbaa05ded4250eddc5
- Size of remote file:
- 6.3 MB
- SHA256:
- 92679703dd07bb5226d23207f8f724779eb0503064465d7c2bbb758289d7dd27
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