Instructions to use peter881122/ocean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter881122/ocean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="peter881122/ocean")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("peter881122/ocean") model = AutoModelForObjectDetection.from_pretrained("peter881122/ocean", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from peter881122/ocean: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/peter881122/ocean/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://peter881122/ocean/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/peter881122/ocean/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 1a6b1ea57bfb1a0f8d40e8b2b7d8f85e6c71cf2d73ea3ed4fa8c505f5af7b09e
- Size of remote file:
- 167 MB
- SHA256:
- 43825aaf9a3281bdebed8c3096df05e4371c822528bb299949bcd1fab1e363fa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.