Instructions to use peter881122/ocean60 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter881122/ocean60 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="peter881122/ocean60")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("peter881122/ocean60") model = AutoModelForObjectDetection.from_pretrained("peter881122/ocean60", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from peter881122/ocean60: direct link, hf CLI and curl.
- Browser
- Download file 26 MB
-
https://huggingface.co/peter881122/ocean60/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://peter881122/ocean60/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/peter881122/ocean60/resolve/main/pytorch_model.bin
26 MB
- Xet hash:
- d34cfecf87b94a217274707dcd46960b1904cff455437a0f85d7e268ebab9e2b
- Size of remote file:
- 26 MB
- SHA256:
- 22f3620d7865fde8992fdb2bdee6f04a7ac1c6bbb47f43f5f2b3e393fe28f50c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.