Object Detection
ultralytics
PyTorch
detectionbench
computer-vision
agriculture
wheat
precision-agriculture
dense-detection
Eval Results (legacy)
Instructions to use dronefreak/gwhd-yolov8s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use dronefreak/gwhd-yolov8s with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("dronefreak/gwhd-yolov8s") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Redesign card: usage moved up top, showcase/banner right under the title, fewer embedded images, leaner citations, shorter showcase mosaic; also fixes missing plot images (BoxP/BoxR/confusion_matrix_normalized) from the original upload
6dc5836 verified Download confusion_matrix_normalized.png from dronefreak/gwhd-yolov8s: direct link, hf CLI and curl.
- Browser
- Download file 102 kB
-
https://huggingface.co/dronefreak/gwhd-yolov8s/resolve/main/confusion_matrix_normalized.png
- Command line
-
hf download hf://dronefreak/gwhd-yolov8s/confusion_matrix_normalized.png
-
curl -L -o confusion_matrix_normalized.png https://huggingface.co/dronefreak/gwhd-yolov8s/resolve/main/confusion_matrix_normalized.png
102 kB

- Xet hash:
- edd3059781b01b86b00ab2695ee953fad81a325f855fc0a5d35143aef5ff6912
- Size of remote file:
- 102 kB
- SHA256:
- ed22ce90c90823c9bfbeb3b6318eb4881233ea25ca38717bb5ddcf4361dab34b
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