Object Detection
ultralytics
TensorBoard
ONNX
yolo
yolov11
drone
uav
imav
robotics
autonomous-landing
helipad-detection
Eval Results (legacy)
Instructions to use blackbeedrones/imav-2025-platform with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use blackbeedrones/imav-2025-platform 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("blackbeedrones/imav-2025-platform", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download train/val/val_batch0_labels.jpg from blackbeedrones/imav-2025-platform: direct link, hf CLI and curl.
- Browser
- Download file 342 kB
-
https://huggingface.co/blackbeedrones/imav-2025-platform/resolve/main/train/val/val_batch0_labels.jpg
- Command line
-
hf download hf://blackbeedrones/imav-2025-platform/train/val/val_batch0_labels.jpg
-
curl -L -o val_batch0_labels.jpg https://huggingface.co/blackbeedrones/imav-2025-platform/resolve/main/train/val/val_batch0_labels.jpg
342 kB

- Xet hash:
- 9952cf3fc26104b1b29ddbcd5644a78f7c7979080a0e4d5972fc52769439aa77
- Size of remote file:
- 342 kB
- SHA256:
- 5c3bafdeaaebdfebbc5d58fa82f2d3156ac708d3477155892483c0dc0df4711f
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