Instructions to use AlessandroFerrante/StreetSignSenseY12n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use AlessandroFerrante/StreetSignSenseY12n with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("AlessandroFerrante/StreetSignSenseY12n", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download metrics/BoxPR_curve.png from AlessandroFerrante/StreetSignSenseY12n: direct link, hf CLI and curl.
- Browser
- Download file 274 kB
-
https://huggingface.co/AlessandroFerrante/StreetSignSenseY12n/resolve/main/metrics/BoxPR_curve.png
- Command line
-
hf download hf://AlessandroFerrante/StreetSignSenseY12n/metrics/BoxPR_curve.png
-
curl -L -o BoxPR_curve.png https://huggingface.co/AlessandroFerrante/StreetSignSenseY12n/resolve/main/metrics/BoxPR_curve.png
274 kB

- Xet hash:
- 804148a06492c86262abbf4be8371ad7562d2ffc4bd4c26a48bc8bc0a167bbc5
- Size of remote file:
- 274 kB
- SHA256:
- 178b9226e48da25f794b617edc8e91747e6ca65289ddf7bf5e1acd439a1851b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.