Instructions to use kiheh85202/yolo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kiheh85202/yolo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="kiheh85202/yolo")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, DPTForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("kiheh85202/yolo") model = DPTForSemanticSegmentation.from_pretrained("kiheh85202/yolo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kiheh85202/yolo: direct link, hf CLI and curl.
- Browser
- Download file 1.37 GB
-
https://huggingface.co/kiheh85202/yolo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kiheh85202/yolo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kiheh85202/yolo/resolve/main/pytorch_model.bin
1.37 GB
- Xet hash:
- d37912e26941ad8b35f56e356bfbcebc72c6f1eca7cde7bdbaf493814b9d9dea
- Size of remote file:
- 1.37 GB
- SHA256:
- cb9106382231b44f797e364a296a98976bf9435b47eecdab2c032b61e6ec7441
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