Object Detection
ultralytics
yolo
instance-segmentation
image-classification
pose-estimation
obb
tracking
yolo11
Eval Results (legacy)
Instructions to use CollectionStudio/YOLO11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use CollectionStudio/YOLO11 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("CollectionStudio/YOLO11", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download yolo11n-pose.pt from CollectionStudio/YOLO11: direct link, hf CLI and curl.
- Browser
- Download file 6.26 MB
-
https://huggingface.co/CollectionStudio/YOLO11/resolve/main/yolo11n-pose.pt
- Command line
-
hf download hf://CollectionStudio/YOLO11/yolo11n-pose.pt
-
curl -L -o yolo11n-pose.pt https://huggingface.co/CollectionStudio/YOLO11/resolve/main/yolo11n-pose.pt
6.26 MB
- Xet hash:
- 30550461dd02d5387b430be31094d5458f8a0a1caf0b57d63ac16447dc757f88
- Size of remote file:
- 6.26 MB
- SHA256:
- 869e83fcdffdc7371fa4e34cd8e51c838cc729571d1635e5141e3075e9319dc0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.