Instructions to use peter881122/ocean52 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter881122/ocean52 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="peter881122/ocean52")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("peter881122/ocean52") model = AutoModelForObjectDetection.from_pretrained("peter881122/ocean52") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 349c87abd0eb2d2196d9ff580d43a5816aa78aba7d91a403d7aa0234b67cc965
- Size of remote file:
- 26 MB
- SHA256:
- ab504ac3b05415ea920b07abb59a54df62c796c62e2eded3cc649d9f85d7904d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.