Object Detection
ultralytics
LiteRT
ONNX
yolov8
agriculture
computer-vision
pest-detection
fall-armyworm
precision-agriculture
kenya
Instructions to use ndunge23/SambaGuard-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use ndunge23/SambaGuard-v2 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("ndunge23/SambaGuard-v2", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download training_samples/train_batch1.jpg from ndunge23/SambaGuard-v2: direct link, hf CLI and curl.
- Browser
- Download file 696 kB
-
https://huggingface.co/ndunge23/SambaGuard-v2/resolve/main/training_samples/train_batch1.jpg
- Command line
-
hf download hf://ndunge23/SambaGuard-v2/training_samples/train_batch1.jpg
-
curl -L -o train_batch1.jpg https://huggingface.co/ndunge23/SambaGuard-v2/resolve/main/training_samples/train_batch1.jpg
696 kB

- Xet hash:
- 9372cc9e6367699d1a961938d561b731851118d7a7ba2f5a98bbb5ef46893892
- Size of remote file:
- 696 kB
- SHA256:
- 77e77f68f1db22cfb2dda7d52d34b16ac623c353fc0191a4fcfa7d022cfd833f
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