Instructions to use zoheb/yolos-small-balloon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zoheb/yolos-small-balloon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="zoheb/yolos-small-balloon")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("zoheb/yolos-small-balloon") model = AutoModelForObjectDetection.from_pretrained("zoheb/yolos-small-balloon", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from zoheb/yolos-small-balloon: direct link, hf CLI and curl.
- Browser
- Download file 123 MB
-
https://huggingface.co/zoheb/yolos-small-balloon/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zoheb/yolos-small-balloon/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zoheb/yolos-small-balloon/resolve/main/pytorch_model.bin
123 MB
- Xet hash:
- 564327648d0c0b87a0ccc36a6ad731540c76be28bf1b44b2f99113519da464a5
- Size of remote file:
- 123 MB
- SHA256:
- 073d1210ad9ce9aa2d7fed6a9fd85eb87522e258b876e23cd7a6e9edd3a3d068
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.