Object Detection
ultralytics
TensorBoard
ONNX
yolo
yolov11
drone
uav
imav
robotics
gate-detection
autonomous-navigation
Eval Results (legacy)
Instructions to use blackbeedrones/imav-2025-gate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use blackbeedrones/imav-2025-gate with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("blackbeedrones/imav-2025-gate") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download train/val_batch0_pred.jpg from blackbeedrones/imav-2025-gate: direct link, hf CLI and curl.
- Browser
- Download file 390 kB
-
https://huggingface.co/blackbeedrones/imav-2025-gate/resolve/main/train/val_batch0_pred.jpg
- Command line
-
hf download hf://blackbeedrones/imav-2025-gate/train/val_batch0_pred.jpg
-
curl -L -o val_batch0_pred.jpg https://huggingface.co/blackbeedrones/imav-2025-gate/resolve/main/train/val_batch0_pred.jpg
390 kB

- Xet hash:
- 261aec5b9c29b7b242884911235735089c5c0aecfb0a9bd289f70f43e60c01fa
- Size of remote file:
- 390 kB
- SHA256:
- 49a436c53b1e23c863e996523820f7d62afbd463cf8ba5deff462a66b60b7ed9
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