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 yolo11m-pose.pt from CollectionStudio/YOLO11: direct link, hf CLI and curl.
- Browser
- Download file 42.5 MB
-
https://huggingface.co/CollectionStudio/YOLO11/resolve/main/yolo11m-pose.pt
- Command line
-
hf download hf://CollectionStudio/YOLO11/yolo11m-pose.pt
-
curl -L -o yolo11m-pose.pt https://huggingface.co/CollectionStudio/YOLO11/resolve/main/yolo11m-pose.pt
42.5 MB
- Xet hash:
- 323a5294ee33949258241b5adba7cbcc68af0d9940bf7ce18c12f939b1a58410
- Size of remote file:
- 42.5 MB
- SHA256:
- 29b17eaf3a3117cbea906090dbedf9159f7c6a49db58ec8b99ed2dfde1cf6eb2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.