Object Detection
ultralytics
English
agriculture
weed-detection
yolo
yolov8
yolo26
precision-agriculture
research
Instructions to use Rmano-Rudeson/weedscan-kz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Rmano-Rudeson/weedscan-kz 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("Rmano-Rudeson/weedscan-kz", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download web/src/test/example.test.ts from Rmano-Rudeson/weedscan-kz: direct link, hf CLI and curl.
- Browser
- Download file 143 Bytes
-
https://huggingface.co/Rmano-Rudeson/weedscan-kz/resolve/main/web/src/test/example.test.ts
- Command line
-
hf download hf://Rmano-Rudeson/weedscan-kz/web/src/test/example.test.ts
-
curl -L -o example.test.ts https://huggingface.co/Rmano-Rudeson/weedscan-kz/resolve/main/web/src/test/example.test.ts
143 Bytes
| import { describe, it, expect } from "vitest"; | |
| describe("example", () => { | |
| it("should pass", () => { | |
| expect(true).toBe(true); | |
| }); | |
| }); | |