Instructions to use Anzhc/Race-Classification-FairFace-YOLOv8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Anzhc/Race-Classification-FairFace-YOLOv8 with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("Anzhc/Race-Classification-FairFace-YOLOv8") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 737845f0ffea115121a30df86e9855a062528e3d3b40ecee9a4f2b517b7aac8c
- Size of remote file:
- 26 MB
- SHA256:
- 409860807160319828dfc1d5db76b5ad9745621b53134e3a3081017dff278e22
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.